{"type":"ScholarlyArticle","title":"DarkFed: A Data-Free Backdoor Attack in Federated Learning","authors":[{"name":"Minghui Li","slug":"minghui-li","given":"Minghui","family":"Li","affiliation":"School of Software Engineering , Huazhong University of Science and Technology"},{"name":"Wei Wan","slug":"wei-wan","given":"Wei","family":"Wan","affiliation":"National Engineering Research Center for Big Data Technology and System; Services Computing Technology and System Lab; Hubei Engineering Research Center on Big Data Security; Hubei Key Laboratory of Distributed System Security; School of Cyber Science and Engineering , Huazhong University of Science and Technology"},{"name":"Yuxuan Ning","slug":"yuxuan-ning","given":"Yuxuan","family":"Ning","affiliation":"School of Computer Science and Technology , Huazhong University of Science and Technology"},{"name":"Shengshan Hu","slug":"shengshan-hu","given":"Shengshan","family":"Hu","affiliation":"National Engineering Research Center for Big Data Technology and System; Services Computing Technology and System Lab; Hubei Engineering Research Center on Big Data Security; Hubei Key Laboratory of Distributed System Security; School of Cyber Science and Engineering , Huazhong University of Science and Technology"},{"name":"Lulu Xue","slug":"lulu-xue","given":"Lulu","family":"Xue","affiliation":"National Engineering Research Center for Big Data Technology and System; Services Computing Technology and System Lab; Hubei Engineering Research Center on Big Data Security; Hubei Key Laboratory of Distributed System Security; School of Cyber Science and Engineering , Huazhong University of Science and Technology"},{"name":"Leo Yu Zhang","slug":"leo-yu-zhang","given":"Leo Yu","family":"Zhang","affiliation":"School of Information and Communication Technology , Griffith University {minghuili , wanwei 0303 ,"},{"name":"Yichen Wang","slug":"yichen-wang","given":"Yichen","family":"Wang","affiliation":"National Engineering Research Center for Big Data Technology and System; Services Computing Technology and System Lab; Hubei Engineering Research Center on Big Data Security; Hubei Key Laboratory of Distributed System Security; School of Cyber Science and Engineering , Huazhong University of Science and Technology"}],"abstract":"Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real clients with main task-related data, which is impractical. In the context of real-world industrial scenarios, even the simplest defense suffices to defend against the state-of-the-art attack, 3DFed. A practical FL backdoor attack remains in a nascent stage of development.\r\n\r\nTo bridge this gap, we present DarkFed. Initially, we emulate a series of fake clients, thereby achieving the attacker proportion typical of academic research scenarios. Given that these emulated fake clients lack genuine training data, we further propose a data-free approach to backdoor FL. \r\nSpecifically, we delve into the feasibility of injecting a backdoor using a shadow dataset. Our exploration reveals that impressive attack performance can be achieved, even when there is a substantial gap between the shadow dataset and the main task dataset. This holds true even when employing synthetic data devoid of any semantic information as the shadow dataset.\r\nSubsequently, we strategically construct a series of covert backdoor updates in an optimized manner, mimicking the properties of benign updates, to evade detection by defenses. A substantial body of empirical evidence validates the tangible effectiveness of DarkFed.","doi":"10.24963/ijcai.2024/491","url":"https://ijcai.org/proceedings/2024/491","pdf":"https://ijcai.org/proceedings/2024/0491.pdf","markdown":"https://ijcai.org/proceedings/2024/491.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/491","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":"Machine Learning","track":"Main Track","number":491,"pages":{"first":4443,"last":4451},"keywords":["Machine Learning: ML: Adversarial machine learning","Machine Learning: ML: Federated learning","Computer Vision: CV: Adversarial learning, adversarial attack and defense methods"],"videos":[],"references":[{"position":1,"raw":"[Bagdasaryan et al., 2020] Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 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