{"type":"ScholarlyArticle","title":"BADFSS: Backdoor Attacks on Federated Self-Supervised Learning","authors":[{"name":"Jiale Zhang","slug":"jiale-zhang","given":"Jiale","family":"Zhang","affiliation":"School of Information Engineering , Yangzhou University , China"},{"name":"Chengcheng Zhu","slug":"chengcheng-zhu","given":"Chengcheng","family":"Zhu","affiliation":"School of Information Engineering , Yangzhou University , China"},{"name":"Di Wu","slug":"di-wu","given":"Di","family":"Wu","affiliation":"School of Mathematics, Physics and Computing , University of Southern Queensland , Australia"},{"name":"Xiaobing Sun","slug":"xiaobing-sun","given":"Xiaobing","family":"Sun","affiliation":"School of Information Engineering , Yangzhou University , China"},{"name":"Jianming Yong","slug":"jianming-yong","given":"Jianming","family":"Yong","affiliation":"School of Business , University of Southern Queensland , Australia"},{"name":"Guodong Long","slug":"guodong-long","given":"Guodong","family":"Long","affiliation":"Australian Artificial Intelligence Institute , FEIT , University of Technology Sydney , Australia"}],"abstract":"Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g., from cameras and phones), often resulting from privacy constraints. Extensive attention has been paid to designing new frameworks or methods that achieve better performance for the SSL-based FL. However, such an effort has not yet taken the security of SSL-based FL into consideration. We aim to explore backdoor attacks in the context of SSL-based FL via an in-depth empirical study. In this paper, we propose a novel backdoor attack BADFSS against SSL-based FL. First, BADFSS learns a backdoored encoder via supervised contrastive learning on poison datasets constructed based on local datasets. Then, BADFSS employs attention alignment to enhance the backdoor effect and maintain the consistency between backdoored and global encoders. Moreover, we perform empirical evaluations of the proposed backdoor attacks on four datasets and compared BADFSS with three existing backdoor attacks that are transferred into federated self-supervised learning. The experiments demonstrate that BADFSS outperforms baseline methods and is effective under various settings.","doi":"10.24963/ijcai.2024/61","url":"https://ijcai.org/proceedings/2024/61","pdf":"https://ijcai.org/proceedings/2024/0061.pdf","markdown":"https://ijcai.org/proceedings/2024/61.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/61","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":61,"pages":{"first":548,"last":558},"keywords":["AI Ethics, Trust, Fairness: ETF: Safety and robustness","Multidisciplinary Topics and Applications: MTA: Security and privacy","AI Ethics, Trust, Fairness: ETF: Trustworthy AI"],"videos":[],"references":[{"position":1,"raw":"[Bagdasaryan et al., 2020] Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 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