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Priv. Secur."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>\n            Federated Learning (FL) is vulnerable to backdoor attacks\u2014especially distributed backdoor attacks (DBA) that are more persistent and stealthy than centralized backdoor attacks. However, we observe that the attack effectiveness of DBA can be largely reduced when encountering rebels, i.e., the agents promising to perform the attack but do not do so. To robustify DBAs, we present\n            <jats:sc>SSRDBA<\/jats:sc>\n            , a secret sharing-inspired robust DBA to FL. To be specific, given a same global trigger as DBA,\n            <jats:sc>SSRDBA<\/jats:sc>\n            carefully divides it into different shares based on secret sharing and exploits these shares to poison local data on malicious devices, respectively.\n            <jats:sc>SSRDBA<\/jats:sc>\n            enjoys several merits, e.g., only partial malicious agents guarantee the reconstruction of the global trigger. Extensive experimental results show that\n            <jats:sc>SSRDBA<\/jats:sc>\n            is more robust to rebels than DBA and can evade the state-of-the-art FL defenses mainly for centralized backdoor attacks. To mitigate\n            <jats:sc>SSRDBA<\/jats:sc>\n            , we further design a novel defense mechanism, termed NFDR, which shows great potential against\n            <jats:sc>SSRDBA<\/jats:sc>\n            on certain independent identically distributed datasets.\n          <\/jats:p>","DOI":"10.1145\/3725814","type":"journal-article","created":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T09:43:39Z","timestamp":1742636619000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A Secret Sharing-Inspired Robust Distributed Backdoor Attack to Federated Learning"],"prefix":"10.1145","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9907-9980","authenticated-orcid":false,"given":"Yuxin","family":"Yang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun, China and Department of Computer Science, Illinois Institute of Technology, Chicago, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7510-4718","authenticated-orcid":false,"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2419-6592","authenticated-orcid":false,"given":"Yuede","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5616-060X","authenticated-orcid":false,"given":"Binghui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Illinois Institute of Technology, Chicago, United States"}]}],"member":"320","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Arevalo Caridad Arroyo","year":"2024","unstructured":"Caridad Arroyo Arevalo, Sayedeh Leila Noorbakhsh, Yun Dong, Yuan Hong, and Binghui Wang. 2024. 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