Toward Efficient and Certified Recovery From Poisoning Attacks in Federated Learning

作者：Yu Jiang, Jiyuan Shen, Ziyao Liu, Chee Wei Tan, Kwok‐Yan Lam · 发表于：IEEE Transactions on Information Forensics and Security · 年份：2025 · DOI：10.1109/tifs.2025.3533907 · 被引用次数：31 · 研究领域：Privacy-Preserving Technologies in Data、Adversarial Robustness in Machine Learning、Cryptography and Data Security

Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients manipulate their updates to affect the global model. Although various methods exist for detecting such clients in FL, identifying malicious clients requires sufficient model updates, and hence by the time malicious clients are detected, FL models have already been poisoned. Thus, a method is needed to recover an accurate global model after malicious clients are identified. Current recovery methods rely on (i) all historical information from participating FL clients and (ii) the initial model unaffected by the malicious clients, both leading to a high demand for storage and computational resources. In this paper, we show that highly effective recovery can still be achieved based on 1) selective historical information rather than all historical information and 2) a historical model that has not been significantly affected by malicious clients rather than the initial model. In this scenario, we can accelerate the recovery speed and decrease memory consumption while maintaining comparable recovery performance. Following this concept, we introduce Crab (Certified Recovery from Poisoning Attacks and Breaches), an efficient and certified recovery method, which relies on selective information storage and adaptive model rollback. Theoretically, we demonstrate that the difference between the global model recovered by Crab and the one recovered by train-from-scratch can be bounded under certain assumptio...