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By compromising some users, the attacker manipulates their local training process, and uploads malicious gradient updates to poison the global model, resulting in the poisoned global model behaving abnormally on the sub-tasks specified by the malicious user. Prior research has proposed various strategies to mitigate backdoor attacks. However, existing FL backdoor defense methods affect the fairness of the FL system, while fair FL performance may not be robust. Motivated by these concerns, in this paper, we propose Self-Awareness Revision (SARS), a personalized FL framework designed to resist backdoor attacks and ensure the fairness of the FL system. SARS consists of two key modules: adaptation feature extraction and knowledge mapping. In the adaptation feature extraction module, benign users can adaptively extract clean global knowledge with self-awareness and self-revision of the backdoor knowledge transferred from the global model. Based on the previous module, users can effectively ensure the correct mapping of clean sample features and labels. Through extensive experimental results, SARS can defend against backdoor attacks and improve the fairness of the FL system by comparing several state-of-the-art FL backdoor defenses or fair FL methods, including FedAvg, Ditto, WeakDP, FoolsGold, and FLAME.<\/jats:p>","DOI":"10.1145\/3678571","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T12:23:32Z","timestamp":1732191812000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["SARS: A Personalized Federated Learning Framework Towards Fairness and Robustness against Backdoor Attacks"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9494-1626","authenticated-orcid":false,"given":"Weibin","family":"Zhang","sequence":"first","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7450-1671","authenticated-orcid":false,"given":"Youpeng","family":"Li","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0103-489X","authenticated-orcid":false,"given":"Lingling","family":"An","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3410-9560","authenticated-orcid":false,"given":"Bo","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4759-8674","authenticated-orcid":false,"given":"Xuyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, Florida, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"International conference on artificial intelligence and statistics. 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