Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape
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Abstract (arXiv)
Despite the promise of Federated Learning (FL) for privacy-preserving model training on distributed data, it remains susceptible to backdoor attacks. These attacks manipulate models by embedding triggers (specific input patterns) in the training data, forcing misclassification as predefined classes during deployment. Traditional single-trigger attacks and recent work on cooperative multiple-trigger attacks, where clients collaborate, highlight limitations in attack realism due to coordination requirements. We investigate a more alarming scenario: non-cooperative multiple-trigger attacks. Here, independent adversaries introduce distinct triggers targeting unique classes. These parallel attacks exploit FL's decentralized nature, making detection difficult. Our experiments demonstrate the alarming vulnerability of FL to such attacks, where individual backdoors can be successfully learned without impacting the main task. This research emphasizes the critical need for robust defenses against diverse backdoor attacks in the evolving FL landscape. While our focus is on empirical analysis, we believe it can guide backdoor research toward more realistic settings, highlighting the crucial role of FL in building robust defenses against diverse backdoor threats. The code is available at urlhttps://anonymous.4open.science/r/nba-980F/.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 31 references that are in the map. First posted on arXiv 5 Jul 2024.
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18 of 31 references.
References · 18
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- 2022DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspectioncited by 78
6 more
- 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
- 2022Marksman Backdoor: Backdoor Attacks with Arbitrary Target Classcited by 32
- 2023On the Vulnerability of Backdoor Defenses for Federated Learningcited by 27
- 2023Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learningcited by 20
- 2024Shortcuts Everywhere and Nowhere: Exploring Multi-Trigger Backdoor Attackscited by 19
- 2022RFLBAT: A Robust Federated Learning Algorithm against Backdoor Attackcited by 9
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