Showing 1–50 of 105 results for author: Passarella, A

Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints

Authors: Samuele Sabella, Chiara Boldrini, Lorenzo Valerio, Marco Conti, Andrea Passarella

Abstract: Decentralized learning is a promising paradigm for collaborative training in mobile and pervasive systems, as it avoids a central coordinator and does not require sharing raw data. Yet, most analyses rely on idealized communication assumptions that break down in wireless settings, where connectivity is intermittent, topology changes due to mobility, and bandwidth is limited. We study decentralized… ▽ More Decentralized learning is a promising paradigm for collaborative training in mobile and pervasive systems, as it avoids a central coordinator and does not require sharing raw data. Yet, most analyses rely on idealized communication assumptions that break down in wireless settings, where connectivity is intermittent, topology changes due to mobility, and bandwidth is limited. We study decentralized averaging under client asynchrony, time-varying contact graphs, and technology-dependent throughput constraints. We implement a fully decentralized protocol that overlaps synchronization with local training and supports partial tensor-level transfers when contacts end early. Using Random Waypoint mobility and multiple wireless technologies (Bluetooth LE, LTE, and Wi-Fi), we quantify how network dynamics and link capacity impact convergence. We identify three operating regimes: (i) inter-contact time largely dictates convergence via mixing, (ii) partial updates are often well tolerated when contacts are frequent, and (iii) very dense contact patterns can trigger contention, reducing effective throughput. These findings provide a practical lens to reason about decentralized learning deployments over realistic wireless systems, highlighting when improving connectivity, increasing bandwidth, or mitigating contention is most impactful. △ Less

Submitted 1 June, 2026; originally announced June 2026.

Comments: Accepted for publication at IEEE SmartComp 2026. This work was partially supported by the PNRR Project SoBigDatait (IR0000013). S. Sabella, C. Boldrini, and M. Conti were partly funded by the PNRR project FAIR (PE00000013), while A. Passarella and L. Valerio were partially supported by the PNRR project RESTART (PE00000001)

Optimal Calibration of Quantum Network Links

Authors: Vinay Kumar, Claudio Cicconetti, Marco Conti, Andrea Passarella

Abstract: The reliable distribution of entanglement is essential for the effective operation of quantum networks. Due to fundamental differences between quantum and classical communication systems, it is necessary to develop specialised algorithms and protocols that also account for quantum-specific constraints. In this work, we focus on the issue of recalibration. As suggested by recent experimental studie… ▽ More The reliable distribution of entanglement is essential for the effective operation of quantum networks. Due to fundamental differences between quantum and classical communication systems, it is necessary to develop specialised algorithms and protocols that also account for quantum-specific constraints. In this work, we focus on the issue of recalibration. As suggested by recent experimental studies, the process of local entanglement generation in a quantum link degrades over time due to environmental changes that have to be estimated and compensated via a calibration operation, during which the link is not available. Therefore, in such a quantum network, every link alternates between an activation period, during which it operates normally, and a calibration period, during which it cannot participate in the end-to-end entanglement distribution, thereby creating a trade-off between link quality (the fidelity of generated pairs, which decays during activation) and availability (the fraction of time the link is usable, which calibration reduces). We develop analytically a protocol for optimally assigning activation periods to each link in linear quantum repeater chains, subject to any general end-to-end fidelity requirements and local initial fidelity thresholds. Building on this foundation, we extend to general quantum networks, where multiple paths may cross at common links, proposing a heuristic approach evaluated in simulations and compared with a benchmark, numerical approach, and theoretical bounds. △ Less

Submitted 16 June, 2026; originally announced June 2026.

Comments: 23 pages, 10 figures

Annotation of Positive vs Negative User Interactions for Social Sign Prediction

Authors: Biancamaria Bombino, Chiara Boldrini, Andrea Passarella, Marco Conti

Abstract: Inferring the sign of social relationships from online interactions is a fundamental challenge in social network analysis. Existing approaches typically rely on sentiment analysis to label individual interactions as positive or negative, then aggregate these labels to assign a sign to the relationship. However, sentiment analysis captures the valence of the content being discussed rather than the… ▽ More Inferring the sign of social relationships from online interactions is a fundamental challenge in social network analysis. Existing approaches typically rely on sentiment analysis to label individual interactions as positive or negative, then aggregate these labels to assign a sign to the relationship. However, sentiment analysis captures the valence of the content being discussed rather than the nature of the relational exchange itself, a conflation that can lead to systematic misclassification. In this paper, we propose a methodology that addresses this limitation by leveraging Large Language Models (LLMs) in a zero-shot setting to identify interaction-level relational signals (specifically, personal praise and personal attacks directed at the interlocutor) as more direct indicators of positive and negative social ties. We evaluate four models spanning open-weight and proprietary architectures (Qwen2.5:7b, Gemma2:9b, GPT-4o, GPT-5.4-mini) across three prompt designs of increasing complexity, on two human-annotated datasets of approximately 298 and 340 texts respectively. Results show that zero-shot LLMs achieve good classification performance on both tasks without any task-specific training data, establishing a practical baseline for relational annotation. Performance differs across tasks: attack detection is robust to prompt design and model choice, while praise detection is more sensitive to both, reflecting the greater subjectivity of positive relational gestures. These findings lay the groundwork for integrating LLM-based relational annotation into sign prediction pipelines. △ Less

Submitted 4 June, 2026; originally announced June 2026.

Layered Ego Networks in Email Communication: From Enron to the Jmail Archive

Authors: Francesco Di Cursi, Chiara Boldrini, Marco Conti, Andrea Passarella

Abstract: Email archives offer a rare view of social relationships through repeated communication, but it remains unclear how well classical ego network layering applies to digital interaction data. This paper compares two public email archives with sharply contrasting structures: Enron, a workplace corpus involving around 150 users, and Jmail, a single-ego archive centered on an exceptionally active focal… ▽ More Email archives offer a rare view of social relationships through repeated communication, but it remains unclear how well classical ego network layering applies to digital interaction data. This paper compares two public email archives with sharply contrasting structures: Enron, a workplace corpus involving around 150 users, and Jmail, a single-ego archive centered on an exceptionally active focal actor whose communication volume is more than twenty times higher than the average Enron user. We ask, in each case, whether Dunbar-like layered organization is recoverable from email communication frequency and how it should be interpreted. For Jmail, we show that extreme communication intensity causes standard layering methods (whether clustering-based or threshold-based) to break down. Jmail is not a broad communication environment with many occasional contacts, but a selective pool of high-interest alters operating on a much higher frequency scale than ordinary email. Once the Dunbar frequency ladder is anchored to the empirical support-clique boundary, a clearer layered structure emerges. Reciprocity analysis confirms that the recovered layers reflect genuine bidirectional relationships rather than artifacts of the focal actor's outgoing activity. Enron serves as a workplace benchmark that grounds the comparison: its ego networks partially reproduce Dunbar-like organization, with stable inner circles and an outermost recovered layer corresponding to Dunbar's affinity group ($\sim50$), confirming that layered structure is recoverable from ordinary organizational email. Overall, the findings show that Dunbar-like organization can be meaningfully studied in email archives, but that selective high-frequency archives require frequency normalization before the layered structure becomes interpretable. △ Less

Submitted 1 June, 2026; originally announced June 2026.

Comments: Under review

Neural Network Compression by Approximate Differential Equivalence

Authors: Ravi Dhiman, Andrea Passarella, Mirco Tribastone, Lorenzo Valerio

Abstract: Neural network compression is commonly achieved by pruning parameters based on local importance scores, e.g., magnitude-based pruning. We propose a complementary approach that compresses models by aggregating neurons with similar functional behavior rather than removing weights independently. Our method encodes a trained network as a polynomial ODE system and applies a lumping method called Approx… ▽ More Neural network compression is commonly achieved by pruning parameters based on local importance scores, e.g., magnitude-based pruning. We propose a complementary approach that compresses models by aggregating neurons with similar functional behavior rather than removing weights independently. Our method encodes a trained network as a polynomial ODE system and applies a lumping method called Approximate Forward Differential Equivalence to identify neurons with approximately matching induced dynamics. A single tolerance parameter, $\varepsilon$, controls the compression level and induces a smooth trade-off between model size and predictive accuracy. We evaluate the method on synthetic datasets derived from nonlinear dynamical systems with known ground-truth behavior and on public regression benchmarks. Across both settings, the proposed approach achieves substantial parameter reduction while preserving accuracy, and consistently compares favorably with magnitude-based pruning and Wanda at similar compression levels. These results suggest that differential equivalence-based aggregation is a principled and effective alternative to conventional weight-centric pruning. △ Less

Submitted 31 May, 2026; originally announced June 2026.

Comments: 19 pages, 4 figures

Dynamic Entanglement Packet Scheduling for Quantum Networks

Authors: Quang-Phong Tran, Claudio Cicconetti, Marco Conti, Andrea Passarella

Abstract: Sharing entanglement among multiple users remains a central challenge for scalable quantum networks. Recent work proposed an on-demand entanglement packet architecture in which a controller uses a Time Division Multiple Access (TDMA) approach to allocate network resources. Quantum nodes are assigned a periodic schedule that probabilistically fulfills application requests for end-to-end entanglemen… ▽ More Sharing entanglement among multiple users remains a central challenge for scalable quantum networks. Recent work proposed an on-demand entanglement packet architecture in which a controller uses a Time Division Multiple Access (TDMA) approach to allocate network resources. Quantum nodes are assigned a periodic schedule that probabilistically fulfills application requests for end-to-end entanglements. The schedule is recomputed periodically using well-known algorithms, such as Earliest Deadline First (EDF). However, a static schedule offers limited flexibility when outcomes are stochastic and arrivals are asynchronous. To overcome this limitation, we propose an online scheduler that dynamically schedules, defers, retries, or drops entanglement distribution reservations. In our simulations, the dynamic scheduler achieves lower completion time, higher completion ratio, and higher throughput than the static baseline. Furthermore, when the network is overloaded, the dynamic scheduler continues to construct deadline-feasible schedules and degrades gracefully. △ Less

Submitted 27 May, 2026; originally announced May 2026.

Comments: Accepted for oral presentation at IEEE QuNAP 2026, a workshop of IEEE INFOCOM 2026

Instruction-Set Architecture for Programmable NV-Center Quantum Repeater Nodes

Authors: Vinay Kumar, Claudio Cicconetti, Riccardo Bassoli, Marco Conti, Andrea Passarella

Abstract: Programmability is increasingly central in emerging quantum network software stacks, yet the node-internal controller-to-hardware interface for quantum repeater devices remains under-specified. We introduce the idea of an instruction-set architecture (ISA) for controller-driven programmability of nitrogen-vacancy (NV) center quantum repeater nodes. Each node consists of an optically interfaced ele… ▽ More Programmability is increasingly central in emerging quantum network software stacks, yet the node-internal controller-to-hardware interface for quantum repeater devices remains under-specified. We introduce the idea of an instruction-set architecture (ISA) for controller-driven programmability of nitrogen-vacancy (NV) center quantum repeater nodes. Each node consists of an optically interfaced electron spin acting as a data qubit and a long-lived nuclear-spin register acting as a control program. We formalize two modes of programmability: (i) deterministic register control, where the nuclear register is initialized in a basis state to select a specific operation on the data qubit; and (ii) coherent register control, where the register is prepared in superposition, enabling coherent combinations of operations beyond classical programmability. Network protocols are expressed as controller-issued instruction vectors, which we illustrate through a compact realization of the BBPSSW purification protocol. We further show that coherent register control enables interferometric diagnostics such as fidelity witnessing and calibration, providing tools unavailable in classical programmability. Finally, we discuss scalability to multi-electron and multi-nuclear spin architectures and connection to Linear combination of unitaries (LCU) and Kraus formulation. △ Less

Submitted 24 February, 2026; v1 submitted 16 February, 2026; originally announced February 2026.

Comments: 10 pages, 5 figures, Author accepted manuscript

Journal ref: Proc. 2026 International Conference on Quantum Communications, Networking, and Computing (QCNC), pp. 1038-1044, 2026

Gender Disparities in StackOverflow's Community-Based Question Answering: A Matter of Quantity versus Quality

Authors: Maddalena Amendola, Cosimo Rulli, Carlos Castillo, Andrea Passarella, Raffaele Perego

Abstract: Community Question-Answering platforms, such as Stack Overflow (SO), are valuable knowledge exchange and problem-solving resources. These platforms incorporate mechanisms to assess the quality of answers and participants' expertise, ideally free from discriminatory biases. However, prior research has highlighted persistent gender biases, raising concerns about the inclusivity and fairness of these… ▽ More Community Question-Answering platforms, such as Stack Overflow (SO), are valuable knowledge exchange and problem-solving resources. These platforms incorporate mechanisms to assess the quality of answers and participants' expertise, ideally free from discriminatory biases. However, prior research has highlighted persistent gender biases, raising concerns about the inclusivity and fairness of these systems. Addressing such biases is crucial for fostering equitable online communities. While previous studies focus on detecting gender bias by comparing male and female user characteristics, they often overlook the interaction between genders, inherent answer quality, and the selection of ``best answers'' by question askers. In this study, we investigate whether answer quality is influenced by gender using a combination of human evaluations and automated assessments powered by Large Language Models. Our findings reveal no significant gender differences in answer quality, nor any substantial influence of gender bias on the selection of ``best answers." Instead, we find that the significant gender disparities in SO's reputation scores are primarily attributable to differences in users' activity levels, e.g., the number of questions and answers they write. Our results have important implications for the design of scoring systems in community question-answering platforms. In particular, reputation systems that heavily emphasize activity volume risk amplifying gender disparities that do not reflect actual differences in answer quality, calling for more equitable design strategies. △ Less

Submitted 30 January, 2026; originally announced January 2026.

DecHW: Heterogeneous Decentralized Federated Learning Exploiting Second-Order Information

Authors: Adnan Ahmad, Chiara Boldrini, Lorenzo Valerio, Andrea Passarella, Marco Conti

Abstract: Decentralized Federated Learning (DFL) is a serverless collaborative machine learning paradigm where devices collaborate directly with neighbouring devices to exchange model information for learning a generalized model. However, variations in individual experiences and different levels of device interactions lead to data and model initialization heterogeneities across devices. Such heterogeneities… ▽ More Decentralized Federated Learning (DFL) is a serverless collaborative machine learning paradigm where devices collaborate directly with neighbouring devices to exchange model information for learning a generalized model. However, variations in individual experiences and different levels of device interactions lead to data and model initialization heterogeneities across devices. Such heterogeneities leave variations in local model parameters across devices that leads to slower convergence. This paper tackles the data and model heterogeneity by explicitly addressing the parameter level varying evidential credence across local models. A novel aggregation approach is introduced that captures these parameter variations in local models and performs robust aggregation of neighbourhood local updates. Specifically, consensus weights are generated via approximation of second-order information of local models on their local datasets. These weights are utilized to scale neighbourhood updates before aggregating them into global neighbourhood representation. In extensive experiments with computer vision tasks, the proposed approach shows strong generalizability of local models at reduced communication costs. △ Less

Submitted 16 January, 2026; originally announced January 2026.

Comments: Funding: SoBigDatait (PNRR IR0000013), FAIR (PNRR PE00000013), RESTART (PNRR PE00000001)

Sparing User Time with a Socially-Aware Independent Metaverse Avatar

Authors: Theofanis P. Raptis, Chiara Boldrini, Marco Conti, Andrea Passarella

Abstract: The Metaverse is redefining digital interactions by merging physical, virtual, and social dimensions, yet its effects on social networking remain largely unexplored. This work examines the role of independent avatars (autonomous digital entities capable of managing social interactions on behalf of users), to optimize social time allocation and reshape Metaverse-based Online Social Networks. We pro… ▽ More The Metaverse is redefining digital interactions by merging physical, virtual, and social dimensions, yet its effects on social networking remain largely unexplored. This work examines the role of independent avatars (autonomous digital entities capable of managing social interactions on behalf of users), to optimize social time allocation and reshape Metaverse-based Online Social Networks. We propose a novel computational model that integrates a quantitative and realistic representation of user social life, grounded in evolutionary anthropology, with a framework for avatar-mediated interactions. Our model quantifies the effectiveness of a partial replacement of in-person interactions with independent avatar interactions. Additionally, it accounts for social conflicts and specific socialization constraints. We leverage our model to explore the benefits and trade-offs of an avatar-augmented social life in the Metaverse. Since the exact problem formulation leads to an NP-hard optimization problem when incorporating avatars into the social network, we tackle this challenge by introducing a heuristic solution. Through simulations, we compare avatar-mediated and non-avatar-mediated social networking, demonstrating the potential of independent avatars to enhance social connectivity and efficiency. Our findings provide a foundation for optimizing Metaverse-based social interactions, as well as useful insights for future digital social network design. △ Less

Submitted 16 January, 2026; originally announced January 2026.

Comments: Supported by PNNR projects SoBigDatait (IR0000013), FAIR (PE00000013), ICSC (CN00000013)

Mind Reading or Misreading? LLMs on the Big Five Personality Test

Authors: Francesco Di Cursi, Chiara Boldrini, Marco Conti, Andrea Passarella

Abstract: We evaluate large language models (LLMs) for automatic personality prediction from text under the binary Five Factor Model (BIG5). Five models -- including GPT-4 and lightweight open-source alternatives -- are tested across three heterogeneous datasets (Essays, MyPersonality, Pandora) and two prompting strategies (minimal vs. enriched with linguistic and psychological cues). Enriched prompts reduc… ▽ More We evaluate large language models (LLMs) for automatic personality prediction from text under the binary Five Factor Model (BIG5). Five models -- including GPT-4 and lightweight open-source alternatives -- are tested across three heterogeneous datasets (Essays, MyPersonality, Pandora) and two prompting strategies (minimal vs. enriched with linguistic and psychological cues). Enriched prompts reduce invalid outputs and improve class balance, but also introduce a systematic bias toward predicting trait presence. Performance varies substantially: Openness and Agreeableness are relatively easier to detect, while Extraversion and Neuroticism remain challenging. Although open-source models sometimes approach GPT-4 and prior benchmarks, no configuration yields consistently reliable predictions in zero-shot binary settings. Moreover, aggregate metrics such as accuracy and macro-F1 mask significant asymmetries, with per-class recall offering clearer diagnostic value. These findings show that current out-of-the-box LLMs are not yet suitable for APPT, and that careful coordination of prompt design, trait framing, and evaluation metrics is essential for interpretable results. △ Less

Submitted 28 November, 2025; originally announced November 2025.

Comments: Funding: SoBigDatait (IR0000013), FAIR (PE00000013), ICSC (CN00000013)

Toward Hybrid COTS-based LiFi/WiFi Networks with QoS Requirements in Mobile Environments

Authors: Emilio Ancillotti, Loreto Pescosolido, Andrea Passarella

Abstract: We consider a hybrid LiFi/WiFi network consisting of commercially available equipment, for mobile scenarios, where WiFi backs up communications, through vertical handovers, in case of insufficient LiFi QoS. When QoS requirements in terms of goodput are defined, tools are needed to anticipate the vertical handover relative to what is possible with standard basic mechanisms, which are only based on… ▽ More We consider a hybrid LiFi/WiFi network consisting of commercially available equipment, for mobile scenarios, where WiFi backs up communications, through vertical handovers, in case of insufficient LiFi QoS. When QoS requirements in terms of goodput are defined, tools are needed to anticipate the vertical handover relative to what is possible with standard basic mechanisms, which are only based on a complete loss of connectivity. We introduce two such mechanisms, based on signal power level readings and CRC-based packet failure ratio, and evaluate their performance in terms of QoS-outage duration, considering as a benchmark an existing baseline solution based on the detection of a connectivity loss. In doing this, we provide insights into the interplay between such mechanisms and the LiFi protocol channel adaptation capabilities. Our experimental results are obtained using a lab-scale testbed equipped with a conveyor belt, which allows us to accurately replicate experiments with devices in motion. With the proposed methods, we achieve QoS outages below one second for a QoS level of 20 Mbps, compared to outage durations of a few seconds obtained with the baseline solution. △ Less

Submitted 31 October, 2025; originally announced November 2025.

Comments: 8 pages, 9 Figures, conference paper

Cascade-driven opinion dynamics on social networks

Authors: Elisabetta Biondi, Chiara Boldrini, Andrea Passarella, Marco Conti

Abstract: Online social networks (OSNs) have transformed the way individuals fulfill their social needs and consume information. As OSNs become increasingly prominent sources for news dissemination, individuals often encounter content that influences their opinions through both direct interactions and broader network dynamics. In this paper, we propose the Friedkin-Johnsen on Cascade (FJC) model, which is,… ▽ More Online social networks (OSNs) have transformed the way individuals fulfill their social needs and consume information. As OSNs become increasingly prominent sources for news dissemination, individuals often encounter content that influences their opinions through both direct interactions and broader network dynamics. In this paper, we propose the Friedkin-Johnsen on Cascade (FJC) model, which is, to the best of our knowledge, is the first attempt to integrate information cascades and opinion dynamics, specifically using the very popular Friedkin-Johnsen model. Our model, validated over real social cascades, highlights how the convergence of socialization and sharing news on these platforms can disrupt opinion evolution dynamics typically observed in offline settings. Our findings demonstrate that these cascades can amplify the influence of central opinion leaders, making them more resistant to divergent viewpoints, even when challenged by a critical mass of dissenting opinions. This research underscores the importance of understanding the interplay between social dynamics and information flow in shaping public discourse in the digital age. △ Less

Submitted 16 March, 2026; v1 submitted 19 June, 2025; originally announced June 2025.

Comments: 12 pages, 9 figures, 2 tables

Journal ref: IEEE Transactions on Computational Social Systems (2026)

The Impact of COVID-19 on Twitter Ego Networks: Structure, Sentiment, and Topics

Authors: Kamer Cekini, Elisabetta Biondi, Chiara Boldrini, Andrea Passarella, Marco Conti

Abstract: Lockdown measures, implemented by governments during the initial phases of the COVID-19 pandemic to reduce physical contact and limit viral spread, imposed significant restrictions on in-person social interactions. Consequently, individuals turned to online social platforms to maintain connections. Ego networks, which model the organization of personal relationships according to human cognitive co… ▽ More Lockdown measures, implemented by governments during the initial phases of the COVID-19 pandemic to reduce physical contact and limit viral spread, imposed significant restrictions on in-person social interactions. Consequently, individuals turned to online social platforms to maintain connections. Ego networks, which model the organization of personal relationships according to human cognitive constraints on managing meaningful interactions, provide a framework for analyzing such dynamics. The disruption of physical contact and the predominant shift of social life online potentially altered the allocation of cognitive resources dedicated to managing these digital relationships. This research aims to investigate the impact of lockdown measures on the characteristics of online ego networks, presumably resulting from this reallocation of cognitive resources. To this end, a large dataset of Twitter users was examined, covering a seven-year period of activity. Analyzing a seven-year Twitter dataset -- including five years pre-pandemic and two years post -- we observe clear, though temporary, changes. During lockdown, ego networks expanded, social circles became more structured, and relationships intensified. Simultaneously, negative interactions increased, and users engaged with a broader range of topics, indicating greater thematic diversity. Once restrictions were lifted, these structural, emotional, and thematic shifts largely reverted to pre-pandemic norms -- suggesting a temporary adaptation to an extraordinary social context. △ Less

Submitted 4 June, 2025; originally announced June 2025.

Comments: Funding: SoBigData.it (IR0000013), SoBigData PPP (101079043), FAIR (PE00000013), SERICS (PE00000014), ICSC (CN00000013)

Towards Robust Expert Finding in Community Question Answering Platforms

Authors: Maddalena Amendola, Andrea Passarella, Raffaele Perego

Abstract: This paper introduces TUEF, a topic-oriented user-interaction model for fair Expert Finding in Community Question Answering (CQA) platforms. The Expert Finding task in CQA platforms involves identifying proficient users capable of providing accurate answers to questions from the community. To this aim, TUEF improves the robustness and credibility of the CQA platform through a more precise Expert F… ▽ More This paper introduces TUEF, a topic-oriented user-interaction model for fair Expert Finding in Community Question Answering (CQA) platforms. The Expert Finding task in CQA platforms involves identifying proficient users capable of providing accurate answers to questions from the community. To this aim, TUEF improves the robustness and credibility of the CQA platform through a more precise Expert Finding component. The key idea of TUEF is to exploit diverse types of information, specifically, content and social information, to identify more precisely experts thus improving the robustness of the task. We assess TUEF through reproducible experiments conducted on a large-scale dataset from StackOverflow. The results consistently demonstrate that TUEF outperforms state-of-the-art competitors while promoting transparent expert identification. △ Less

Submitted 4 March, 2025; originally announced March 2025.

Journal ref: Advances in Information Retrieval, Springer Nature Switzerland, 2024, 152--168

Uncoordinated Access to Serverless Computing in MEC Systems for IoT

Authors: Claudio Cicconetti, Marco Conti, Andrea Passarella

Abstract: Edge computing is a promising solution to enable low-latency IoT applications, by shifting computation from remote data centers to local devices, less powerful but closer to the end user devices. However, this creates the challenge on how to best assign clients to edge nodes offering compute capabilities. So far, two antithetical architectures are proposed: centralized resource orchestration or di… ▽ More Edge computing is a promising solution to enable low-latency IoT applications, by shifting computation from remote data centers to local devices, less powerful but closer to the end user devices. However, this creates the challenge on how to best assign clients to edge nodes offering compute capabilities. So far, two antithetical architectures are proposed: centralized resource orchestration or distributed overlay. In this work we explore a third way, called uncoordinated access, which consists in letting every device exploring multiple opportunities, to opportunistically embrace the heterogeneity of network and load conditions towards diverse edge nodes. In particular, our contribution is intended for emerging serverless IoT applications, which do not have a state on the edge nodes executing tasks. We model the proposed system as a set of M/M/1 queues and show that it achieves a smaller jitter delay than single edge node allocation. Furthermore, we compare uncoordinated access with state-of-the-art centralized and distributed alternatives in testbed experiments under more realistic conditions. Based on the results, our proposed approach, which requires a tiny fraction of the complexity of the alternatives in both the device and network components, is very effective in using the network resources, while incurring only a small penalty in terms of increased compute load and high percentiles of delay. △ Less

Submitted 1 March, 2025; originally announced March 2025.

Journal ref: Computer Networks, Volume 172, 2020, 107184, ISSN 1389-1286

Distributed Data Access in Industrial Edge Networks

Authors: Theofanis P. Raptis, Andrea Passarella, Marco Conti

Abstract: Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primarily on centralized data storage, might n… ▽ More Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primarily on centralized data storage, might not be able to cope with the scalability and real time requirements of Industry 4.0 environments, while distributed solutions are increasingly being explored. In this paper, we introduce the problem of distributed data access in multi-hop wireless industrial edge deployments, whereby a set of consumer nodes needs to access data stored in a set of data cache nodes, satisfying the industrial data access delay requirements and at the same time maximizing the network lifetime. We prove that the introduced problem is computationally intractable and, after formulating the objective function, we design a two-step algorithm in order to address it. We use an open testbed with real devices for conducting an experimental investigation on the performance of the algorithm. Then, we provide two online improvements, so that the data distribution can dynamically change before the first node in the network runs out of energy. We compare the performance of the methods via simulations for different numbers of network nodes and data consumers, and we show significant lifetime prolongation and increased energy efficiency when employing the method which is using only decentralized low-power wireless communication instead of the method which is using also centralized local area wireless communication. △ Less

Submitted 28 February, 2025; originally announced February 2025.

Comments: This work was funded by the EC through the FoF-RIA Project AUTOWARE (No. 723909)

Journal ref: IEEE Journal on Selected Areas in Communications, vol. 38, no. 5, pp. 915-927, May 2020

The Built-In Robustness of Decentralized Federated Averaging to Bad Data

Authors: Samuele Sabella, Chiara Boldrini, Lorenzo Valerio, Andrea Passarella, Marco Conti

Abstract: Decentralized federated learning (DFL) enables devices to collaboratively train models over complex network topologies without relying on a central controller. In this setting, local data remains private, but its quality and quantity can vary significantly across nodes. The extent to which a fully decentralized system is vulnerable to poor-quality or corrupted data remains unclear, but several fac… ▽ More Decentralized federated learning (DFL) enables devices to collaboratively train models over complex network topologies without relying on a central controller. In this setting, local data remains private, but its quality and quantity can vary significantly across nodes. The extent to which a fully decentralized system is vulnerable to poor-quality or corrupted data remains unclear, but several factors could contribute to potential risks. Without a central authority, there can be no unified mechanism to detect or correct errors, and each node operates with a localized view of the data distribution, making it difficult for the node to assess whether its perspective aligns with the true distribution. Moreover, models trained on low-quality data can propagate through the network, amplifying errors. To explore the impact of low-quality data on DFL, we simulate two scenarios with degraded data quality -- one where the corrupted data is evenly distributed in a subset of nodes and one where it is concentrated on a single node -- using a decentralized implementation of FedAvg. Our results reveal that averaging-based decentralized learning is remarkably robust to localized bad data, even when the corrupted data resides in the most influential nodes of the network. Counterintuitively, this robustness is further enhanced when the corrupted data is concentrated on a single node, regardless of its centrality in the communication network topology. This phenomenon is explained by the averaging process, which ensures that no single node -- however central -- can disproportionately influence the overall learning process. △ Less

Submitted 4 June, 2025; v1 submitted 25 February, 2025; originally announced February 2025.

Comments: Accepted at IJCNN 2025. Funding: SoBigData PPP (101079043), SoBigData.it (PNRR IR0000013), FAIR (PNRR PE00000013), RESTART (PNRR PE00000001)

Energy Efficient Network Path Reconfiguration for Industrial Field Data

Authors: Theofanis P. Raptis, Andrea Passarella, Marco Conti

Abstract: Energy efficiency and reliability are vital design requirements of recent industrial networking solutions. Increased energy consumption, poor data access rates and unpredictable end-to-end data access latencies are catastrophic when transferring high volumes of critical industrial data in strict temporal deadlines. These requirements might become impossible to meet later on, due to node failures,… ▽ More Energy efficiency and reliability are vital design requirements of recent industrial networking solutions. Increased energy consumption, poor data access rates and unpredictable end-to-end data access latencies are catastrophic when transferring high volumes of critical industrial data in strict temporal deadlines. These requirements might become impossible to meet later on, due to node failures, or excessive degradation of the performance of wireless links. In this paper, we focus on maintaining the network functionality required by the industrial, best effort, low-latency applications after such events, by sacrificing latency guarantees to improve energy consumption and reliability. We avoid continuously recomputing the network configuration centrally, by designing an energy efficient, local and distributed path reconfiguration method. Specifically, given the operational parameters required by the applications, our method locally reconfigures the data distribution paths, when a network node fails. Additionally, our method also regulates the return to an operational state of nodes that have been offline in the past. We compare the performance of our method through simulations to the performance of other state of the art protocols and we demonstrate performance gains in terms of energy consumption, data delivery success rate, and in some cases, end-to-end data access latency. We conclude by providing some emerging key insights which can lead to further performance improvements. △ Less

Submitted 21 February, 2025; originally announced February 2025.

Comments: This work was funded by the EC through the FoF-RIA Project AUTOWARE (No. 723909). arXiv admin note: substantial text overlap with arXiv:1803.10971

Journal ref: Computer Communications, Volume 158, 15 May 2020, Pages 1-9

Optimal Popularity-based Transmission Range Selection for D2D-supported Content Delivery

Authors: Loreto Pescosolido, Andrea Passarella, Marco Conti

Abstract: Considering device-to-device (D2D) wireless links as a virtual extension of 5G (and beyond) cellular networks to deliver popular contents has been proposed as an interesting approach to reduce energy consumption, congestion, and bandwidth usage at the network edge. In the scenario of multiple users in a region independently requesting some popular content, there is a major potential for energy con… ▽ More Considering device-to-device (D2D) wireless links as a virtual extension of 5G (and beyond) cellular networks to deliver popular contents has been proposed as an interesting approach to reduce energy consumption, congestion, and bandwidth usage at the network edge. In the scenario of multiple users in a region independently requesting some popular content, there is a major potential for energy consumption reduction exploiting D2D communications. In this scenario, we consider the problem of selecting the maximum allowed transmission range (or equivalently the maximum transmit power) for the D2D links that support the content delivery process. We show that, for a given maximum allowed D2D energy consumption, a considerable reduction of the cellular infrastructure energy consumption can be achieved by selecting the maximum D2D transmission range as a function of content class parameters such as popularity and delay-tolerance, compared to a uniform selection across different content classes. Specifically, we provide an analytical model that can be used to estimate the energy consumption (for small delay tolerance) and thus to set the optimal transmission range. We validate the model via simulations and study the energy gain that our approach allows to obtain. Our results show that the proposed approach to the maximum D2D transmission range selection allows a reduction of the overall energy consumption in the range of 30% to 55%, compared to a selection of the maximum D2D transmission range oblivious to popularity and delay tolerance. △ Less

Submitted 20 February, 2025; originally announced February 2025.

Comments: 6 pages, 6 figures, conference paper

Journal ref: Proceedings of the 2020 ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM '20), November 16-20, 2020, Alicante, Spain. ACM, New York, NY, USA

SLICES, a scientific instrument for the networking community

Authors: Serge Fdida, Nikos Makris, Thanasis Korakis, Raffaele Bruno, Andrea Passarella, Panayiotis Andreou, Bartosz Belter, Cedric Crettaz, Walid Dabbous, Yuri Demchenko, Raymond Knopp

Abstract: A science is defined by a set of encyclopedic knowledge related to facts or phenomena following rules or evidenced by experimentally-driven observations. Computer Science and in particular computer networks is a relatively new scientific domain maturing over years and adopting the best practices inherited from more fundamental disciplines. The design of past, present and future networking componen… ▽ More A science is defined by a set of encyclopedic knowledge related to facts or phenomena following rules or evidenced by experimentally-driven observations. Computer Science and in particular computer networks is a relatively new scientific domain maturing over years and adopting the best practices inherited from more fundamental disciplines. The design of past, present and future networking components and architectures have been assisted, among other methods, by experimentally-driven research and in particular by the deployment of test platforms, usually named as testbeds. However, often experimentally-driven networking research used scattered methodologies, based on ad-hoc, small-sized testbeds, producing hardly repeatable results. We believe that computer networks needs to adopt a more structured methodology, supported by appropriate instruments, to produce credible experimental results supporting radical and incremental innovations. This paper reports lessons learned from the design and operation of test platforms for the scientific community dealing with digital infrastructures. We introduce the SLICES initiative as the outcome of several years of evolution of the concept of a networking test platform transformed into a scientific instrument. We address the challenges, requirements and opportunities that our community is facing to manage the full research-life cycle necessary to support a scientific methodology. △ Less

Submitted 13 February, 2025; originally announced February 2025.

Journal ref: Volume 193, 1 September 2022, Pages 189-203

Quantum Internet: Technologies, Protocols, and Research Challenges

Authors: Vinay Kumar, Claudio Cicconetti, Marco Conti, Andrea Passarella

Abstract: As the field of the quantum internet advances, a comprehensive guide to navigate its complexities has become increasingly crucial. While quantum computing shares foundational principles with the quantum internet, distinguishing between the two is essential for further development and deeper understanding. This work systematically introduces the quantum internet by discussing its importance, core c… ▽ More As the field of the quantum internet advances, a comprehensive guide to navigate its complexities has become increasingly crucial. While quantum computing shares foundational principles with the quantum internet, distinguishing between the two is essential for further development and deeper understanding. This work systematically introduces the quantum internet by discussing its importance, core components, operational mechanisms, anticipated timeline for viability, key contributors, major challenges, and future directions. Additionally, it presents the fundamental concepts of quantum mechanics that underpin the technology, offering a clear and targeted overview intended for researchers and industry professionals and laying the groundwork for future innovations and research in the field. △ Less

Submitted 21 August, 2025; v1 submitted 30 January, 2025; originally announced February 2025.

Comments: 50 pages, 11 figures, Author accepted manuscript

Journal ref: Int. J. Networked Distrib. Comput., vol. 13, no. 2, article 22, 2025

A Herd of Young Mastodonts: the User-Centered Footprints of Newcomers After Twitter Acquisition

Authors: Francesco Di Cursi, Chiara Boldrini, Andrea Passarella, Marco Conti

Abstract: The tremendous success of major Online Social Networks (OSNs) platforms has raised increasing concerns about negative phenomena, such as mass control, fake news, and echo chambers. In addition, the increasingly strict control over users' data by platform owners questions their trustworthiness as open interaction tools. These trends and, notably, the recent drastic change in X (formerly Twitter) po… ▽ More The tremendous success of major Online Social Networks (OSNs) platforms has raised increasing concerns about negative phenomena, such as mass control, fake news, and echo chambers. In addition, the increasingly strict control over users' data by platform owners questions their trustworthiness as open interaction tools. These trends and, notably, the recent drastic change in X (formerly Twitter) policies and data accessibility through public APIs, have fuelled significant migration of users towards Fediverse platforms (primarily Mastodon). In this work, we provide an initial analysis of the microscopic properties of Mastodon users' social structures. Specifically, according to the Ego network model, we analyse interaction patterns between a large set of users (egos) and the other users they interact with (alters) to characterise the properties of those users' ego networks. As was observed previously in other OSNs, we found a quite regular structure compatible with the reference Dunbar's Ego Network model. Quite interestingly, our results show clear signs of ego network formation during the initial diffusion of a social networking tool, coherent with the recent surge of Mastodon activity. Therefore, our analysis motivates the use of Mastodon as an open "big data microscope" to characterise human social behaviour, making it a prime candidate to replace those OSN platforms that, unfortunately, cannot be used anymore for this purpose. △ Less

Submitted 20 December, 2024; originally announced December 2024.

Comments: 871042 - "SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics"; 101079043 - "SoBigData RI PPP: SoBigData RI Preparatory Phase Project"; IR0000013 - "SoBigData.it - Strengthening the Italian RI for Social Mining and Big Data Analytics"; CN00000013 - "ICSC - National Centre for HPC, Big Data and Quantum Computing"; PE00000013 - "FAIR"

Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions

Authors: Mirko Nardi, Lorenzo Valerio, Andrea Passarella

Abstract: Federated Learning (FL) enables decentralized machine learning while preserving data privacy, making it ideal for sensitive applications where data cannot be shared. While FL has been widely studied in supervised contexts, its application to unsupervised learning remains underdeveloped. This work introduces FedCRef, a novel unsupervised federated learning method designed to uncover all underlying… ▽ More Federated Learning (FL) enables decentralized machine learning while preserving data privacy, making it ideal for sensitive applications where data cannot be shared. While FL has been widely studied in supervised contexts, its application to unsupervised learning remains underdeveloped. This work introduces FedCRef, a novel unsupervised federated learning method designed to uncover all underlying data distributions across decentralized clients without requiring labels. This task, known as Federated Clustering, presents challenges due to heterogeneous, non-uniform data distributions and the lack of centralized coordination. Unlike previous methods that assume a one-cluster-per-client setup or require prior knowledge of the number of clusters, FedCRef generalizes to multi-cluster-per-client scenarios. Clients iteratively refine their data partitions while discovering all distinct distributions in the system. The process combines local clustering, model exchange and evaluation via reconstruction error analysis, and collaborative refinement within federated groups of similar distributions to enhance clustering accuracy. Extensive evaluations on four public datasets (EMNIST, KMNIST, Fashion-MNIST and KMNIST49) show that FedCRef successfully identifies true global data distributions, achieving an average local accuracy of up to 95%. The method is also robust to noisy conditions, scalable, and lightweight, making it suitable for resource-constrained edge devices. △ Less

Submitted 8 January, 2026; v1 submitted 20 August, 2024; originally announced August 2024.

FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher

Authors: Alessio Mora, Lorenzo Valerio, Paolo Bellavista, Andrea Passarella

Abstract: Federated Learning (FL) enables the collaborative training of machine learning models without requiring centralized collection of user data. To comply with the right to be forgotten, FL clients should be able to request the removal of their data contributions from the global model. In this paper, we propose FedQUIT, a novel unlearning algorithm that operates directly on client devices that request… ▽ More Federated Learning (FL) enables the collaborative training of machine learning models without requiring centralized collection of user data. To comply with the right to be forgotten, FL clients should be able to request the removal of their data contributions from the global model. In this paper, we propose FedQUIT, a novel unlearning algorithm that operates directly on client devices that request to remove its contribution. Our method leverages knowledge distillation to remove the influence of the target client's data from the global model while preserving its generalization ability. FedQUIT adopts a teacher-student framework, where a modified version of the current global model serves as a virtual teacher and the client's model acts as the student. The virtual teacher is obtained by manipulating the global model's outputs on forget data, penalizing the confidence assigned to the true class while preserving relationships among outputs of non-true classes, to simultaneously induce forgetting and retain useful knowledge. As a result, FedQUIT achieves unlearning without making any additional assumption over the standard FedAvg protocol. Evaluation across diverse datasets, data heterogeneity levels, and model architectures shows that FedQUIT achieves superior or comparable unlearning efficacy compared to six state-of-the-art methods, while significantly reducing cumulative communication and computational overhead relative to retraining from scratch. △ Less

Submitted 13 April, 2026; v1 submitted 14 August, 2024; originally announced August 2024.

Routing in Quantum Networks with End-to-End Knowledge

Authors: Vinay Kumar, Claudio Cicconetti, Marco Conti, Andrea Passarella

Abstract: Given the diverse array of physical systems available for quantum computing and the absence of a well-defined quantum internet protocol stack, the design and optimisation of quantum networking protocols remain largely unexplored. To address this, we introduce an approach that facilitates the establishment of paths capable of delivering end-to-end fidelity above a specified threshold, without requi… ▽ More Given the diverse array of physical systems available for quantum computing and the absence of a well-defined quantum internet protocol stack, the design and optimisation of quantum networking protocols remain largely unexplored. To address this, we introduce an approach that facilitates the establishment of paths capable of delivering end-to-end fidelity above a specified threshold, without requiring detailed knowledge of the quantum network properties. In this study, we define algorithms that are specific instances of this approach and evaluate them in comparison to Dijkstra shortest path algorithm and a fully knowledge-aware algorithm through simulations. Our results demonstrate that one of the proposed algorithms consistently outperforms the other methods in delivering paths above the fidelity threshold, across various network topologies and the number of source-destination pairs involved, while maintaining significant levels of fairness among the users and being robust to inaccurate estimations of the expected end-to-end fidelity. △ Less

Submitted 21 August, 2025; v1 submitted 19 July, 2024; originally announced July 2024.

Comments: 17 pages, 19 figures, Author accepted manuscript

Journal ref: IET Quantum Communication, 6(1): e70000 (2025)

Understanding and Addressing Gender Bias in Expert Finding Task

Authors: Maddalena Amendola, Carlos Castillo, Andrea Passarella, Raffaele Perego

Abstract: The Expert Finding (EF) task is critical in community Question&Answer (CQ&A) platforms, significantly enhancing user engagement by improving answer quality and reducing response times. However, biases, especially gender biases, have been identified in these platforms. This study investigates gender bias in state-of-the-art EF models and explores methods to mitigate it. Utilizing a comprehensive da… ▽ More The Expert Finding (EF) task is critical in community Question&Answer (CQ&A) platforms, significantly enhancing user engagement by improving answer quality and reducing response times. However, biases, especially gender biases, have been identified in these platforms. This study investigates gender bias in state-of-the-art EF models and explores methods to mitigate it. Utilizing a comprehensive dataset from StackOverflow, the largest community in the StackExchange network, we conduct extensive experiments to analyze how EF models' candidate identification processes influence gender representation. Our findings reveal that models relying on reputation metrics and activity levels disproportionately favor male users, who are more active on the platform. This bias results in the underrepresentation of female experts in the ranking process. We propose adjustments to EF models that incorporate a more balanced preprocessing strategy and leverage content-based and social network-based information, with the aim to provide a fairer representation of genders among identified experts. Our analysis shows that integrating these methods can significantly enhance gender balance without compromising model accuracy. To the best of our knowledge, this study is the first to focus on detecting and mitigating gender bias in EF methods. △ Less

Submitted 7 July, 2024; originally announced July 2024.

Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering Platforms

Authors: Maddalena Amendola, Andrea Passarella, Raffaele Perego

Abstract: Online Community Question Answering (CQA) platforms have become indispensable tools for users seeking expert solutions to their technical queries. The effectiveness of these platforms relies on their ability to identify and direct questions to the most knowledgeable users within the community, a process known as Expert Finding (EF). EF accuracy is crucial for increasing user engagement and the rel… ▽ More Online Community Question Answering (CQA) platforms have become indispensable tools for users seeking expert solutions to their technical queries. The effectiveness of these platforms relies on their ability to identify and direct questions to the most knowledgeable users within the community, a process known as Expert Finding (EF). EF accuracy is crucial for increasing user engagement and the reliability of provided answers. Despite recent advancements in EF methodologies, blending the diverse information sources available on CQA platforms for effective expert identification remains challenging. In this paper, we present TUEF, a Topic-oriented User-Interaction model for Expert Finding, which aims to fully and transparently leverage the heterogeneous information available within online question-answering communities. TUEF integrates content and social data by constructing a multi-layer graph that maps out user relationships based on their answering patterns on specific topics. By combining these sources of information, TUEF identifies the most relevant and knowledgeable users for any given question and ranks them using learning-to-rank techniques. Our findings indicate that TUEF's topic-oriented model significantly enhances performance, particularly in large communities discussing well-defined topics. Additionally, we show that the interpretable learning-to-rank algorithm integrated into TUEF offers transparency and explainability with minimal performance trade-offs. The exhaustive experiments conducted on six different CQA communities of Stack Exchange show that TUEF outperforms all competitors with a minimum performance boost of 42.42% in P@1, 32.73% in NDCG@3, 21.76% in R@5, and 29.81% in MRR, excelling in both the evaluation approaches present in the previous literature. △ Less

Submitted 4 July, 2024; originally announced July 2024.

Social Isolation, Digital Connection: COVID-19's Impact on Twitter Ego Networks

Authors: Kamer Cekini, Elisabetta Biondi, Chiara Boldrini, Andrea Passarella, Marco Conti

Abstract: One of the most impactful measures to fight the COVID-19 pandemic in its early first years was the lockdown, implemented by governments to reduce physical contact among people and minimize opportunities for the virus to spread. As people were compelled to limit their physical interactions and stay at home, they turned to online social platforms to alleviate feelings of loneliness. Ego networks rep… ▽ More One of the most impactful measures to fight the COVID-19 pandemic in its early first years was the lockdown, implemented by governments to reduce physical contact among people and minimize opportunities for the virus to spread. As people were compelled to limit their physical interactions and stay at home, they turned to online social platforms to alleviate feelings of loneliness. Ego networks represent how people organize their relationships due to human cognitive constraints that impose limits on meaningful interactions among people. Physical contacts were disrupted during the lockdown, causing socialization to shift entirely online, leading to a shift in socialization into online platforms. Our research aimed to investigate the impact of lockdown measures on online ego network structures potentially caused by the increase of cognitive expenses in online social networks. In particular, we examined a large Twitter dataset of users, covering 7 years of their activities. We found that during the lockdown, there was an increase in network sizes and a richer structure in social circles, with relationships becoming more intimate. Moreover, we observe that, after the lockdown measures were relaxed, these features returned to their pre-lockdown values. △ Less

Submitted 1 July, 2024; originally announced July 2024.

Comments: Work supported by SoBigData.it (N. IR0000013), ICSC (N. CN00000013), FAIR (N. PE00000013)

Applying the Ego Network Model to Cross-Target Stance Detection

Authors: Jack Tacchi, Parisa Jamadi Khiabani, Arkaitz Zubiaga, Chiara Boldrini, Andrea Passarella

Abstract: Understanding human interactions and social structures is an incredibly important task, especially in such an interconnected world. One task that facilitates this is Stance Detection, which predicts the opinion or attitude of a text towards a target entity. Traditionally, this has often been done mainly via the use of text-based approaches, however, recent work has produced a model (CT-TN) that le… ▽ More Understanding human interactions and social structures is an incredibly important task, especially in such an interconnected world. One task that facilitates this is Stance Detection, which predicts the opinion or attitude of a text towards a target entity. Traditionally, this has often been done mainly via the use of text-based approaches, however, recent work has produced a model (CT-TN) that leverages information about a user's social network to help predict their stance, outperforming certain cross-target text-based approaches. Unfortunately, the data required for such graph-based approaches is not always available. This paper proposes two novel tools for Stance Detection: the Ego Network Model (ENM) and the Signed Ego Network Model (SENM). These models are founded in anthropological and psychological studies and have been used within the context of social network analysis and related tasks (e.g., link prediction). Stance Detection predictions obtained using these features achieve a level of accuracy similar to the graph-based features used by CT-TN while requiring less and more easily obtainable data. In addition to this, the performances of the inner and outer circles of the ENM, representing stronger and weaker social ties, respectively are compared. Surprisingly, the outer circles, which contain more numerous but less intimate connections, are more useful for predicting stance. △ Less

Submitted 1 July, 2024; originally announced July 2024.

Comments: Accepted at ASONAM 2024

Unveiling Cognitive Constraints in Language Production: Extracting and Validating the Active Ego Network of Words

Authors: Kilian Ollivier, Chiara Boldrini, Andrea Passarella, Marco Conti

Abstract: The "ego network of words" model captures structural properties in language production associated with cognitive constraints. While previous research focused on the layer-based structure and its semantic properties, this paper argues that an essential element, the concept of an active network, is missing. The active part of the ego network of words only includes words that are regularly used by in… ▽ More The "ego network of words" model captures structural properties in language production associated with cognitive constraints. While previous research focused on the layer-based structure and its semantic properties, this paper argues that an essential element, the concept of an active network, is missing. The active part of the ego network of words only includes words that are regularly used by individuals, akin to the ego networks in the social domain, where the active part includes relationships regularly nurtured by individuals and hence demanding cognitive effort. In this work, we define a methodology for extracting the active part of the ego network of words and validate it using interview transcripts and tweets. The robustness of our method to varying input data sizes and temporal stability is demonstrated. We also demonstrate that without the active network concept (and a tool for properly extracting the active network from data), the "ego network of words" model is not able to properly estimate the cognitive effort involved and it becomes vulnerable to the amount of data considered (leading to the disappearance of the layered structure in large datasets). Our results are well-aligned with prior analyses of the ego network of words, where the limitation of the data collected led automatically (and implicitly) to approximately consider the active part of the network only. Moreover, the validation on the transcripts dataset (MediaSum) highlights the generalizability of the model across diverse domains and the ingrained cognitive constraints in language usage. △ Less

Submitted 24 June, 2024; originally announced June 2024.

Comments: Accepted for publication in IEEE Transactions on Computational Social Systems. Partly supported by projects SoBigData.it (PNRR IR0000013) and ICSC (PNRR CN00000013)

Energy-Efficient Deployment of Stateful FaaS Vertical Applications on Edge Data Networks

Authors: Claudio Cicconetti, Raffaele Bruno, Andrea Passarella

Abstract: 5G and beyond support the deployment of vertical applications, which is particularly appealing in combination with network slicing and edge computing to create a logically isolated environment for executing customer services. Even if serverless computing has gained significant interest as a cloud-native technology its adoption at the edge is lagging, especially because of the need to support state… ▽ More 5G and beyond support the deployment of vertical applications, which is particularly appealing in combination with network slicing and edge computing to create a logically isolated environment for executing customer services. Even if serverless computing has gained significant interest as a cloud-native technology its adoption at the edge is lagging, especially because of the need to support stateful tasks, which are commonplace in, e.g., cognitive services, but not fully amenable to being deployed on limited and decentralized computing infrastructures. In this work, we study the emerging paradigm of stateful Function as a Service (FaaS) with lightweight task abstractions in WebAssembly. Specifically, we assess the implications of deploying inter-dependent tasks with an internal state on edge computing resources using a stateless vs. stateful approach and then derive a mathematical model to estimate the energy consumption of a workload with given characteristics, considering the power used for both processing and communication. The model is used in extensive simulations to determine the impact of key factors and assess the energy trade-offs of stateless vs. stateful. △ Less

Submitted 7 May, 2024; originally announced May 2024.

Comments: Accepted for presentation at IEEE ICCCN 2024

Robustness of Decentralised Learning to Nodes and Data Disruption

Authors: Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio, Andrea Passarella, Marco Conti, János Kertész

Abstract: In the vibrant landscape of AI research, decentralised learning is gaining momentum. Decentralised learning allows individual nodes to keep data locally where they are generated and to share knowledge extracted from local data among themselves through an interactive process of collaborative refinement. This paradigm supports scenarios where data cannot leave local nodes due to privacy or sovereign… ▽ More In the vibrant landscape of AI research, decentralised learning is gaining momentum. Decentralised learning allows individual nodes to keep data locally where they are generated and to share knowledge extracted from local data among themselves through an interactive process of collaborative refinement. This paradigm supports scenarios where data cannot leave local nodes due to privacy or sovereignty reasons or real-time constraints imposing proximity of models to locations where inference has to be carried out. The distributed nature of decentralised learning implies significant new research challenges with respect to centralised learning. Among them, in this paper, we focus on robustness issues. Specifically, we study the effect of nodes' disruption on the collective learning process. Assuming a given percentage of "central" nodes disappear from the network, we focus on different cases, characterised by (i) different distributions of data across nodes and (ii) different times when disruption occurs with respect to the start of the collaborative learning task. Through these configurations, we are able to show the non-trivial interplay between the properties of the network connecting nodes, the persistence of knowledge acquired collectively before disruption or lack thereof, and the effect of data availability pre- and post-disruption. Our results show that decentralised learning processes are remarkably robust to network disruption. As long as even minimum amounts of data remain available somewhere in the network, the learning process is able to recover from disruptions and achieve significant classification accuracy. This clearly varies depending on the remaining connectivity after disruption, but we show that even nodes that remain completely isolated can retain significant knowledge acquired before the disruption. △ Less

Submitted 30 June, 2025; v1 submitted 3 May, 2024; originally announced May 2024.

Comments: Supported by the H2020 HumaneAI Net (952026), CHIST-ERA-19-XAI010 SAI, PNRR - M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 FAIR, PNRR - M4C2 - Investimento 1.3, Partenariato Esteso PE00000001 RESTART

Journal ref: Computer Communications, 108250 (2025)

Optimizing Risk-averse Human-AI Hybrid Teams

Authors: Andrew Fuchs, Andrea Passarella, Marco Conti

Abstract: We anticipate increased instances of humans and AI systems working together in what we refer to as a hybrid team. The increase in collaboration is expected as AI systems gain proficiency and their adoption becomes more widespread. However, their behavior is not error-free, making hybrid teams a very suitable solution. As such, we consider methods for improving performance for these teams of humans… ▽ More We anticipate increased instances of humans and AI systems working together in what we refer to as a hybrid team. The increase in collaboration is expected as AI systems gain proficiency and their adoption becomes more widespread. However, their behavior is not error-free, making hybrid teams a very suitable solution. As such, we consider methods for improving performance for these teams of humans and AI systems. For hybrid teams, we will refer to both the humans and AI systems as agents. To improve team performance over that seen for agents operating individually, we propose a manager which learns, through a standard Reinforcement Learning scheme, how to best delegate, over time, the responsibility of taking a decision to any of the agents. We further guide the manager's learning so they also minimize how many changes in delegation are made resulting from undesirable team behavior. We demonstrate the optimality of our manager's performance in several grid environments which include failure states which terminate an episode and should be avoided. We perform our experiments with teams of agents with varying degrees of acceptable risk, in the form of proximity to a failure state, and measure the manager's ability to make effective delegation decisions with respect to its own risk-based constraints, then compare these to the optimal decisions. Our results show our manager can successfully learn desirable delegations which result in team paths near/exactly optimal with respect to path length and number of delegations. △ Less

Submitted 13 March, 2024; originally announced March 2024.

Impact of network topology on the performance of Decentralized Federated Learning

Authors: Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio, Andrea Passarella, Marco Conti

Abstract: Fully decentralized learning is gaining momentum for training AI models at the Internet's edge, addressing infrastructure challenges and privacy concerns. In a decentralized machine learning system, data is distributed across multiple nodes, with each node training a local model based on its respective dataset. The local models are then shared and combined to form a global model capable of making… ▽ More Fully decentralized learning is gaining momentum for training AI models at the Internet's edge, addressing infrastructure challenges and privacy concerns. In a decentralized machine learning system, data is distributed across multiple nodes, with each node training a local model based on its respective dataset. The local models are then shared and combined to form a global model capable of making accurate predictions on new data. Our exploration focuses on how different types of network structures influence the spreading of knowledge - the process by which nodes incorporate insights gained from learning patterns in data available on other nodes across the network. Specifically, this study investigates the intricate interplay between network structure and learning performance using three network topologies and six data distribution methods. These methods consider different vertex properties, including degree centrality, betweenness centrality, and clustering coefficient, along with whether nodes exhibit high or low values of these metrics. Our findings underscore the significance of global centrality metrics (degree, betweenness) in correlating with learning performance, while local clustering proves less predictive. We highlight the challenges in transferring knowledge from peripheral to central nodes, attributed to a dilution effect during model aggregation. Additionally, we observe that central nodes exert a pull effect, facilitating the spread of knowledge. In examining degree distribution, hubs in Barabasi-Albert networks positively impact learning for central nodes but exacerbate dilution when knowledge originates from peripheral nodes. Finally, we demonstrate the formidable challenge of knowledge circulation outside of segregated communities. △ Less

Submitted 28 February, 2024; originally announced February 2024.

Comments: Funding: H2020 HumaneAI Net (Grant N. 952026), CHIST-ERA SAI (CHIST-ERA-19-XAI010), PNRR FAIR (PE00000013), PNRR RESTART (PE00000001). arXiv admin note: text overlap with arXiv:2307.15947

On the Joint Effect of Culture and Discussion Topics on X (Twitter) Signed Ego Networks

Authors: Jack Tacchi, Chiara Boldrini, Andrea Passarella, Marco Conti

Abstract: Humans are known to structure social relationships according to certain patterns, such as the Ego Network Model (ENM). These patterns result from our innate cognitive limits and can therefore be observed in the vast majority of large human social groups. Until recently, the main focus of research was the structural characteristics of this model. The main aim of this paper is to complement previous… ▽ More Humans are known to structure social relationships according to certain patterns, such as the Ego Network Model (ENM). These patterns result from our innate cognitive limits and can therefore be observed in the vast majority of large human social groups. Until recently, the main focus of research was the structural characteristics of this model. The main aim of this paper is to complement previous findings with systematic and data-driven analyses on the positive and negative sentiments of social relationships, across different cultures, communities and topics of discussion. A total of 26 datasets were collected for this work. It was found that contrary to previous findings, the influence of culture is not easily ``overwhelmed'' by that of the topic of discussion. However, more specific and polarising topics do lead to noticeable increases in negativity across all cultures. These negativities also appear to be stable across the different levels of the ENM, which contradicts previous hypotheses. Finally, the number of generic topics being discussed between users seems to be a good predictor of the overall positivity of their relationships. △ Less

Submitted 27 August, 2025; v1 submitted 28 February, 2024; originally announced February 2024.

Comments: Funding: H2020 SoBigData++ (Grant Agreement n.871042), PNRR SoBigData.it (Prot. IR0000013), PNRR ICSC (CN00000013), PNRR FAIR (PE00000013)

Optimizing Delegation in Collaborative Human-AI Hybrid Teams

Authors: Andrew Fuchs, Andrea Passarella, Marco Conti

Abstract: When humans and autonomous systems operate together as what we refer to as a hybrid team, we of course wish to ensure the team operates successfully and effectively. We refer to team members as agents. In our proposed framework, we address the case of hybrid teams in which, at any time, only one team member (the control agent) is authorized to act as control for the team. To determine the best sel… ▽ More When humans and autonomous systems operate together as what we refer to as a hybrid team, we of course wish to ensure the team operates successfully and effectively. We refer to team members as agents. In our proposed framework, we address the case of hybrid teams in which, at any time, only one team member (the control agent) is authorized to act as control for the team. To determine the best selection of a control agent, we propose the addition of an AI manager (via Reinforcement Learning) which learns as an outside observer of the team. The manager learns a model of behavior linking observations of agent performance and the environment/world the team is operating in, and from these observations makes the most desirable selection of a control agent. We restrict the manager task by introducing a set of constraints. The manager constraints indicate acceptable team operation, so a violation occurs if the team enters a condition which is unacceptable and requires manager intervention. To ensure minimal added complexity or potential inefficiency for the team, the manager should attempt to minimize the number of times the team reaches a constraint violation and requires subsequent manager intervention. Therefore our manager is optimizing its selection of authorized agents to boost overall team performance while minimizing the frequency of manager intervention. We demonstrate our manager performance in a simulated driving scenario representing the case of a hybrid team of agents composed of a human driver and autonomous driving system. We perform experiments for our driving scenario with interfering vehicles, indicating the need for collision avoidance and proper speed control. Our results indicate a positive impact of our manager, with some cases resulting in increased team performance up to ~187% that of the best solo agent performance. △ Less

Submitted 25 August, 2024; v1 submitted 8 February, 2024; originally announced February 2024.

Keep Your Friends Close, and Your Enemies Closer: Structural Properties of Negative Relationships on Twitter

Authors: Jack Tacchi, Chiara Boldrini, Andrea Passarella, Marco Conti

Abstract: The Ego Network Model (ENM) is a model for the structural organisation of relationships, rooted in evolutionary anthropology, that is found ubiquitously in social contexts. It takes the perspective of a single user (Ego) and organises their contacts (Alters) into a series of (typically 5) concentric circles of decreasing intimacy and increasing size. Alters are sorted based on their tie strength t… ▽ More The Ego Network Model (ENM) is a model for the structural organisation of relationships, rooted in evolutionary anthropology, that is found ubiquitously in social contexts. It takes the perspective of a single user (Ego) and organises their contacts (Alters) into a series of (typically 5) concentric circles of decreasing intimacy and increasing size. Alters are sorted based on their tie strength to the Ego, however, this is difficult to measure directly. Traditionally, the interaction frequency has been used as a proxy but this misses the qualitative aspects of connections, such as signs (i.e. polarity), which have been shown to provide extremely useful information. However, the sign of an online social relationship is usually an implicit piece of information, which needs to be estimated by interaction data from Online Social Networks (OSNs), making sign prediction in OSNs a research challenge in and of itself. This work aims to bring the ENM into the signed networks domain by investigating the interplay of signed connections with the ENM. This paper delivers 2 main contributions. Firstly, a new and data-efficient method of signing relationships between individuals using sentiment analysis and, secondly, we provide an in-depth look at the properties of Signed Ego Networks (SENs), using 9 Twitter datasets of various categories of users. We find that negative connections are generally over-represented in the active part of the Ego Networks, suggesting that Twitter greatly over-emphasises negative relationships with respect to "offline" social networks. Further, users who use social networks for professional reasons have an even greater share of negative connections. △ Less

Submitted 29 January, 2024; originally announced January 2024.

On the Potential of an Independent Avatar to Augment Metaverse Social Networks

Authors: Theofanis P. Raptis, Chiara Boldrini, Marco Conti, Andrea Passarella

Abstract: We present a computational modelling approach which targets capturing the specifics on how to virtually augment a Metaverse user's available social time capacity via using an independent and autonomous version of her digital representation in the Metaverse. We motivate why this is a fundamental building block to model large-scale social networks in the Metaverse, and emerging properties herein. We… ▽ More We present a computational modelling approach which targets capturing the specifics on how to virtually augment a Metaverse user's available social time capacity via using an independent and autonomous version of her digital representation in the Metaverse. We motivate why this is a fundamental building block to model large-scale social networks in the Metaverse, and emerging properties herein. We envision a Metaverse-focused extension of the traditional avatar concept: An avatar can be as well programmed to operate independently when its user is not controlling it directly, thus turning it into an agent-based digital human representation. This way, we highlight how such an independent avatar could help its user to better navigate their social relationships and optimize their socializing time in the Metaverse by (partly) offloading some interactions to the avatar. We model the setting and identify the characteristic variables by using selected concepts from social sciences: ego networks, social presence, and social cues. Then, we formulate the problem of maximizing the user's non-avatar-mediated spare time as a linear optimization. Finally, we analyze the feasible region of the problem and we present some initial insights on the spare time that can be achieved for different parameter values of the avatar-mediated interactions. △ Less

Submitted 9 May, 2024; v1 submitted 12 December, 2023; originally announced December 2023.

Comments: Supported by the projects: Piano Nazionale di Ripresa e Resilienza IR0000013 - "SoBigData.it", Partenariato Esteso PE00000013 - "FAIR", Centro Nazionale CN00000013 - "ICSC"

Journal ref: IEEE ICCCN 2024

Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity

Authors: Lorenzo Valerio, Chiara Boldrini, Andrea Passarella, János Kertész, Márton Karsai, Gerardo Iñiguez

Abstract: Federated Learning (FL) is a well-known framework for successfully performing a learning task in an edge computing scenario where the devices involved have limited resources and incomplete data representation. The basic assumption of FL is that the devices communicate directly or indirectly with a parameter server that centrally coordinates the whole process, overcoming several challenges associat… ▽ More Federated Learning (FL) is a well-known framework for successfully performing a learning task in an edge computing scenario where the devices involved have limited resources and incomplete data representation. The basic assumption of FL is that the devices communicate directly or indirectly with a parameter server that centrally coordinates the whole process, overcoming several challenges associated with it. However, in highly pervasive edge scenarios, the presence of a central controller that oversees the process cannot always be guaranteed, and the interactions (i.e., the connectivity graph) between devices might not be predetermined, resulting in a complex network structure. Moreover, the heterogeneity of data and devices further complicates the learning process. This poses new challenges from a learning standpoint that we address by proposing a communication-efficient Decentralised Federated Learning (DFL) algorithm able to cope with them. Our solution allows devices communicating only with their direct neighbours to train an accurate model, overcoming the heterogeneity induced by data and different training histories. Our results show that the resulting local models generalise better than those trained with competing approaches, and do so in a more communication-efficient way. △ Less

Submitted 7 December, 2023; originally announced December 2023.

Comments: Supported by the H2020 HumaneAI Net (#952026), H2020 INFRAIA-01-2018-2019 SoBigData++ (#871042), and by the CHIST-ERA-19-XAI010 SAI projects, FWF (grant No. I 5205). Also funded by PNRR MUR Partenariato Esteso PE00000013 FAIR, PNRR MUR Partenariato Esteso PE00000001 - "RESTART"

Routing in Quantum Repeater Networks with Mixed Efficiency Figures

Authors: Vinay Kumar, Claudio Cicconetti, Marco Conti, Andrea Passarella

Abstract: This study explores an approach to routing in quantum networks, which targets practical scenarios for quantum networks, mirroring real-world classical networks. By addressing practical constraints, we examine the impact of heterogeneous nodes with mixed efficiency figures on quantum network performance. In particular, we focus on some key parameters in an operational quantum network such as the fr… ▽ More This study explores an approach to routing in quantum networks, which targets practical scenarios for quantum networks, mirroring real-world classical networks. By addressing practical constraints, we examine the impact of heterogeneous nodes with mixed efficiency figures on quantum network performance. In particular, we focus on some key parameters in an operational quantum network such as the fraction of nodes with a higher efficiency (called high-quality), path establishment order, end-to-end fidelity, i.e., a measure of the quality of the end-to-end entanglement established. Our simulations show that incorporating knowledge of node quality not only helps boost the fidelity of some of the routing paths but also reduces the number of blocked paths in the quantum network. The study also highlights the critical role of the fraction of high-quality nodes in end-to-end fidelity and explores the trade-offs between upgrading all nodes to high quality or retaining a subset of lower-quality nodes. △ Less

Submitted 21 August, 2025; v1 submitted 13 October, 2023; originally announced October 2023.

Comments: 6 pages, 7 figures, Author accepted manuscript

Journal ref: Proc. IEEE Future Networks World Forum (FNWF), pp. 198-203, 2024

Exploring the Impact of Disrupted Peer-to-Peer Communications on Fully Decentralized Learning in Disaster Scenarios

Authors: Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio, Andrea Passarella, Marco Conti

Abstract: Fully decentralized learning enables the distribution of learning resources and decision-making capabilities across multiple user devices or nodes, and is rapidly gaining popularity due to its privacy-preserving and decentralized nature. Importantly, this crowdsourcing of the learning process allows the system to continue functioning even if some nodes are affected or disconnected. In a disaster s… ▽ More Fully decentralized learning enables the distribution of learning resources and decision-making capabilities across multiple user devices or nodes, and is rapidly gaining popularity due to its privacy-preserving and decentralized nature. Importantly, this crowdsourcing of the learning process allows the system to continue functioning even if some nodes are affected or disconnected. In a disaster scenario, communication infrastructure and centralized systems may be disrupted or completely unavailable, hindering the possibility of carrying out standard centralized learning tasks in these settings. Thus, fully decentralized learning can help in this case. However, transitioning from centralized to peer-to-peer communications introduces a dependency between the learning process and the topology of the communication graph among nodes. In a disaster scenario, even peer-to-peer communications are susceptible to abrupt changes, such as devices running out of battery or getting disconnected from others due to their position. In this study, we investigate the effects of various disruptions to peer-to-peer communications on decentralized learning in a disaster setting. We examine the resilience of a decentralized learning process when a subset of devices drop from the process abruptly. To this end, we analyze the difference between losing devices holding data, i.e., potential knowledge, vs. devices contributing only to the graph connectivity, i.e., with no data. Our findings on a Barabasi-Albert graph topology, where training data is distributed across nodes in an IID fashion, indicate that the accuracy of the learning process is more affected by a loss of connectivity than by a loss of data. Nevertheless, the network remains relatively robust, and the learning process can achieve a good level of accuracy. △ Less

Submitted 4 October, 2023; originally announced October 2023.

Comments: Accepted at IEEE ICT-DM 2023

Optimizing delegation between human and AI collaborative agents

Authors: Andrew Fuchs, Andrea Passarella, Marco Conti

Abstract: In the context of humans operating with artificial or autonomous agents in a hybrid team, it is essential to accurately identify when to authorize those team members to perform actions. Given past examples where humans and autonomous systems can either succeed or fail at tasks, we seek to train a delegating manager agent to make delegation decisions with respect to these potential performance defi… ▽ More In the context of humans operating with artificial or autonomous agents in a hybrid team, it is essential to accurately identify when to authorize those team members to perform actions. Given past examples where humans and autonomous systems can either succeed or fail at tasks, we seek to train a delegating manager agent to make delegation decisions with respect to these potential performance deficiencies. Additionally, we cannot always expect the various agents to operate within the same underlying model of the environment. It is possible to encounter cases where the actions and transitions would vary between agents. Therefore, our framework provides a manager model which learns through observations of team performance without restricting agents to matching dynamics. Our results show our manager learns to perform delegation decisions with teams of agents operating under differing representations of the environment, significantly outperforming alternative methods to manage the team. △ Less

Submitted 11 October, 2023; v1 submitted 26 September, 2023; originally announced September 2023.

Comments: This work has been accepted to the 'Towards Hybrid Human-Machine Learning and Decision Making (HLDM)' workshop at ECML PKDD 2023

The effect of network topologies on fully decentralized learning: a preliminary investigation

Authors: Luigi Palmieri, Lorenzo Valerio, Chiara Boldrini, Andrea Passarella

Abstract: In a decentralized machine learning system, data is typically partitioned among multiple devices or nodes, each of which trains a local model using its own data. These local models are then shared and combined to create a global model that can make accurate predictions on new data. In this paper, we start exploring the role of the network topology connecting nodes on the performance of a Machine L… ▽ More In a decentralized machine learning system, data is typically partitioned among multiple devices or nodes, each of which trains a local model using its own data. These local models are then shared and combined to create a global model that can make accurate predictions on new data. In this paper, we start exploring the role of the network topology connecting nodes on the performance of a Machine Learning model trained through direct collaboration between nodes. We investigate how different types of topologies impact the "spreading of knowledge", i.e., the ability of nodes to incorporate in their local model the knowledge derived by learning patterns in data available in other nodes across the networks. Specifically, we highlight the different roles in this process of more or less connected nodes (hubs and leaves), as well as that of macroscopic network properties (primarily, degree distribution and modularity). Among others, we show that, while it is known that even weak connectivity among network components is sufficient for information spread, it may not be sufficient for knowledge spread. More intuitively, we also find that hubs have a more significant role than leaves in spreading knowledge, although this manifests itself not only for heavy-tailed distributions but also when "hubs" have only moderately more connections than leaves. Finally, we show that tightly knit communities severely hinder knowledge spread. △ Less

Submitted 29 July, 2023; originally announced July 2023.

Human-AI Coevolution

Authors: Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-Laszlo Barabasi, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, Janos Kertesz, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex Sandy Pentland, John Shawe-Taylor, Alessandro Vespignani

Abstract: Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices on online pla… ▽ More Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices on online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often ``unintended'' social outcomes. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., technical, epistemological, legal and socio-political. △ Less

Submitted 3 May, 2024; v1 submitted 23 June, 2023; originally announced June 2023.

Cultural Differences in Signed Ego Networks on Twitter: An Investigatory Analysis

Authors: Jack Tacchi, Chiara Boldrini, Andrea Passarella, Marco Conti

Abstract: Human social behaviour has been observed to adhere to certain structures. One such structure, the Ego Network Model (ENM), has been found almost ubiquitously in human society. Recently, this model has been extended to include signed connections. While the unsigned ENM has been rigorously observed for decades, the signed version is still somewhat novel and lacks the same breadth of observation. The… ▽ More Human social behaviour has been observed to adhere to certain structures. One such structure, the Ego Network Model (ENM), has been found almost ubiquitously in human society. Recently, this model has been extended to include signed connections. While the unsigned ENM has been rigorously observed for decades, the signed version is still somewhat novel and lacks the same breadth of observation. Therefore, the main aim of this paper is to examine this signed structure across various categories of individuals from a swathe of culturally distinct regions. Minor differences in the distribution of signs across the SENM can be observed between cultures. However, these can be overwhelmed when the network is centred around a specific topic. Indeed, users who are engaged with specific themes display higher levels of negativity in their networks. This effect is further supported by a significant negative correlation between the number of "general" topics discussed in a network and that network's percentage of negative connections. These findings suggest that the negativity of communications and relationships on Twitter are very dependent on the topics being discussed and, furthermore, these relationships are more likely to be negative when they are based around a specific topic. △ Less

Submitted 17 May, 2023; originally announced May 2023.

Comments: This work was partially supported by the following projects: H2020 HumaneAI-Net (952026), SoBigData++ (871042), CHIST-ERA-19-XAI010 SAI, Partenariato Esteso PE00000013 - "FAIR", Centro Nazionale CN00000013 - "ICSC". In Companion Proceedings of the ACM Web Conference 2023

Qkd@Edge: Online Admission Control of Edge Applications with QKD-secured Communications

Authors: Claudio Cicconetti, Marco Conti, Andrea Passarella

Abstract: Quantum Key Distribution (QKD) enables secure communications via the exchange of cryptographic keys exploiting the properties of quantum mechanics. Nowadays the related technology is mature enough for production systems, thus field deployments of QKD networks are expected to appear in the near future, starting from local/metropolitan settings, where edge computing is already a thriving reality. In… ▽ More Quantum Key Distribution (QKD) enables secure communications via the exchange of cryptographic keys exploiting the properties of quantum mechanics. Nowadays the related technology is mature enough for production systems, thus field deployments of QKD networks are expected to appear in the near future, starting from local/metropolitan settings, where edge computing is already a thriving reality. In this paper, we investigate the interplay of resource allocation in the QKD network vs. edge nodes, which creates unique research challenges. After modeling mathematically the problem, we propose practical online policies for admitting edge application requests, which also select the edge node for processing and the path in the QKD network. Our simulation results provide initial insights into this emerging topic and lead the way to upcoming studies on the subject. △ Less

Submitted 25 June, 2023; v1 submitted 3 May, 2023; originally announced May 2023.

Comments: Paper accepted for presentation at IEEE SMARTCOMP 2023 (version with acronyms fixed)

Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems

Authors: Andrew Fuchs, Andrea Passarella, Marco Conti

Abstract: Given an increasing prevalence of intelligent systems capable of autonomous actions or augmenting human activities, it is important to consider scenarios in which the human, autonomous system, or both can exhibit failures as a result of one of several contributing factors (e.g. perception). Failures for either humans or autonomous agents can lead to simply a reduced performance level, or a failure… ▽ More Given an increasing prevalence of intelligent systems capable of autonomous actions or augmenting human activities, it is important to consider scenarios in which the human, autonomous system, or both can exhibit failures as a result of one of several contributing factors (e.g. perception). Failures for either humans or autonomous agents can lead to simply a reduced performance level, or a failure can lead to something as severe as injury or death. For our topic, we consider the hybrid human-AI teaming case where a managing agent is tasked with identifying when to perform a delegation assignment and whether the human or autonomous system should gain control. In this context, the manager will estimate its best action based on the likelihood of either (human, autonomous) agent failure as a result of their sensing capabilities and possible deficiencies. We model how the environmental context can contribute to, or exacerbate, the sensing deficiencies. These contexts provide cases where the manager must learn to attribute capabilities to suitability for decision-making. As such, we demonstrate how a Reinforcement Learning (RL) manager can correct the context-delegation association and assist the hybrid team of agents in outperforming the behavior of any agent working in isolation. △ Less

Submitted 17 March, 2023; v1 submitted 2 March, 2023; originally announced March 2023.

Journal ref: Sensors 2023, 23, 3409

Service Differentiation and Fair Sharing in Distributed Quantum Computing

Authors: Claudio Cicconetti, Marco Conti, Andrea Passarella

Abstract: In the future, quantum computers will become widespread and a network of quantum repeaters will provide them with end-to-end entanglement of remote quantum bits. As a result, a pervasive quantum computation infrastructure will emerge, which will unlock several novel applications, including distributed quantum computing, that is the pooling of resources on multiple computation nodes to address prob… ▽ More In the future, quantum computers will become widespread and a network of quantum repeaters will provide them with end-to-end entanglement of remote quantum bits. As a result, a pervasive quantum computation infrastructure will emerge, which will unlock several novel applications, including distributed quantum computing, that is the pooling of resources on multiple computation nodes to address problem instances that are unattainable by any individual quantum computer. In this paper, we first investigate the issue of service differentiation in this new environment. Then, we define the problem of how to select which computation nodes should participate in each pool, so as to achieve a fair share of the quantum network resources available. The analysis is performed via an open source simulator and the results are fully and readily available. △ Less

Submitted 10 January, 2023; originally announced January 2023.

Comments: Submitted to Elsevier for possible journal publication. arXiv admin note: text overlap with arXiv:2203.05844

Journal ref: Pervasive and Mobile Computing, available online 8 February 2023, 101758

Social Search: retrieving information in Online Social Platforms -- A Survey

Authors: Maddalena Amendola, Andrea Passarella, Raffaele Perego

Abstract: Social Search research deals with studying methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new t… ▽ More Social Search research deals with studying methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users' preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users' social profiles and behaviors (as they can be inferred from data available on social platforms) to optimize their information needs further. △ Less

Submitted 13 September, 2023; v1 submitted 28 September, 2022; originally announced September 2022.