Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients upload manipulated updates to degrade the performance of the global model. Although detection methods can identify and remove malicious clients, the model remains affected. Retraining from scratch is effective but costly, and existing unlearning methods remain unsatisfactory in both effectiveness and efficiency. We propose Federated Adversarial Unlearning (FAUN), a lightweight framework that retains only a short window of malicious clients' updates and employs adversarial optimization on a proxy dataset to derive updates that eliminate malicious directions. Applying these updates for a few unlearning round...
| Authors | Wenwei Zhao, Xiaowen Li, Yao Liu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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