<?xml version="1.0" encoding="US-ASCII"?>
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<article key="journals/tits/LiuLZLQCZCZL26" mdate="2026-07-26">
<author orcid="0000-0001-6044-5392">Yuange Liu</author>
<author orcid="0009-0004-1929-0332">Yuru Liu</author>
<author orcid="0000-0001-9800-1068">Weishan Zhang</author>
<author orcid="0009-0002-8202-7882">Daobin Luo</author>
<author orcid="0009-0005-5349-4598">Qiao Qiao</author>
<author orcid="0000-0001-8287-2942">Shaohua Cao</author>
<author orcid="0000-0002-6518-1573">Baoyu Zhang</author>
<author orcid="0000-0002-3346-769X">Tao Chen 0023</author>
<author orcid="0000-0001-5235-0748">Hongwei Zhao</author>
<author orcid="0000-0002-0762-6562">Xiaoli Li 0001</author>
<title>Eliminate Conflicts and Attacks: Fair and Robust Federated Learning for Anomaly Detection of Charging Stations.</title>
<year>2026</year>
<month>March</month>
<pages>3731-3743</pages>
<volume>27</volume>
<journal>IEEE Trans. Intell. Transp. Syst.</journal>
<number>3</number>
<ee>https://doi.org/10.1109/TITS.2025.3579885</ee>
<url>db/journals/tits/tits27.html#LiuLZLQCZCZL26</url>
<stream>streams/journals/tits</stream>
</article>
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