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Backdoor security in federated learning: A survey of attacks and defenses
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Ke XU1, Yu ZHAO1, Shiyuan XU2, Xue CHEN2, Qiang LI3, Yu GUO1, *
Science & Technology Review | 2026, 44(14) : 158 - 172
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Science & Technology Review | 2026, 44(14): 158-172
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Backdoor security in federated learning: A survey of attacks and defenses
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Ke XU1, Yu ZHAO1, Shiyuan XU2, Xue CHEN2, Qiang LI3, Yu GUO1, *
Affiliations
  • 1School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
  • 2Department of Computer Science, the University of Hong Kong, Hong Kong 999077, China
  • 3China Industrial Control Systems Cyber Emergency Response Team, Beijing 100040, China
Published: 2026-07-28 doi: 10.3981/j.issn.1000-7857.2025.07.00104
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Federated learning, as a distributed machine learning paradigm, enables collaborative multi-party learning while preserving data privacy, and has been widely deployed in various critical domains. However, its distributed nature also renders it highly vulnerable to backdoor attacks, posing serious security threats. This paper first provides a detailed analysis of the methodological taxonomies and evolutionary directions of backdoor attacks and defenses in the field of federated learning, and summarizes the latest research advances in related areas. Backdoor attack research is shifting toward realistic strategies that simultaneously pursue stealth, persistence, and low data dependency, giving rise to a variety of novel attack vectors. Meanwhile, backdoor defenses are increasingly pursuing greater universality against diverse attack types, as well as a balance between computational overhead and defensive strength within the federated learning environment. Finally, this paper reveals that backdoor attacks and defenses in federated learning are facing increasingly complex challenges, and calls for a proactive shift toward more systematic and integrated designs. On the attack side, research should pivot toward multi-objective balanced attack strategies; on the defense side, emphasis should be placed on intelligent and adaptive mechanisms, while integrating technologies such as privacy-preserving computing to strengthen security guarantees, thereby facilitating its deployment in real-world federated learning environments.

federated learning  /  backdoor attack  /  backdoor defense  /  data security
Ke XU, Yu ZHAO, Shiyuan XU, Xue CHEN, Qiang LI, Yu GUO. Backdoor security in federated learning: A survey of attacks and defenses[J]. Science & Technology Review, 2026 , 44 (14) : 158 -172 . DOI: 10.3981/j.issn.1000-7857.2025.07.00104
Year 2026 volume 44 Issue 14
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Article Info
doi: 10.3981/j.issn.1000-7857.2025.07.00104
  • Receive Date:2025-07-22
  • Online Date:2026-08-19
  • Published:2026-07-28
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  • Received:2025-07-22
  • Revised:2026-01-05
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Affiliations
    1School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
    2Department of Computer Science, the University of Hong Kong, Hong Kong 999077, China
    3China Industrial Control Systems Cyber Emergency Response Team, Beijing 100040, China
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小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
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