Act in Collusion: A Persistent Distributed Multi-Target Backdoor in Federated Learning

• URL: http://arxiv.org/abs/2411.03926v1
• Date: Wed, 06 Nov 2024 13:57:53 GMT
• Title: Act in Collusion: A Persistent Distributed Multi-Target Backdoor in Federated Learning
• Authors: Tao Liu, Wu Yang, Chen Xu, Jiguang Lv, Huanran Wang, Yuhang Zhang, Shuchun Xu, Dapeng Man,
• Abstract summary: Federated learning is vulnerable to backdoor attacks due to its distributed nature. We propose a more practical threat model for federated learning: the distributed multi-target backdoor. We show that 30 rounds after the attack, Attack Success rates of three different backdoors from various clients remain above 93%.
• Score: 5.91728247370845
• License:
• Abstract: Federated learning, a novel paradigm designed to protect data privacy, is vulnerable to backdoor attacks due to its distributed nature. Current research often designs attacks based on a single attacker with a single backdoor, overlooking more realistic and complex threats in federated learning. We propose a more practical threat model for federated learning: the distributed multi-target backdoor. In this model, multiple attackers control different clients, embedding various triggers and targeting different classes, collaboratively implanting backdoors into the global model via central aggregation. Empirical validation shows that existing methods struggle to maintain the effectiveness of multiple backdoors in the global model. Our key insight is that similar backdoor triggers cause parameter conflicts and injecting new backdoors disrupts gradient directions, significantly weakening some backdoors performance. To solve this, we propose a Distributed Multi-Target Backdoor Attack (DMBA), ensuring efficiency and persistence of backdoors from different malicious clients. To avoid parameter conflicts, we design a multi-channel dispersed frequency trigger strategy to maximize trigger differences. To mitigate gradient interference, we introduce backdoor replay in local training to neutralize conflicting gradients. Extensive validation shows that 30 rounds after the attack, Attack Success Rates of three different backdoors from various clients remain above 93%. The code will be made publicly available after the review period.

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