提出轻量级联邦去学习框架，快速清除恶意客户端影响。
Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
- 仅保留恶意客户端近期更新，用对抗优化生成清除恶意方向的更新。
- 只需少量轮次即恢复模型性能，攻击成功率接近零。
- 适合需快速修复被污染模型的工业级联邦学习场景。
联邦学习易受投毒攻击，恶意客户端上传篡改更新会降低全局模型性能。尽管检测方法可识别并移除恶意客户端，模型仍受损害。从头重训有效但代价高昂，现有去学习方法在效果与效率上均不理想。本文提出联邦对抗去学习（FAUN），仅保留恶意客户端短时更新，通过代理数据集上的对抗优化生成消除恶意方向的更新。经数轮去学习后进行良性微调，可快速消除恶意影响并稳定恢复模型。三个典型数据集上的实验表明，FAUN的恢复效果接近重训，但所需轮次远少，且将攻击成功率降至接近零，证实其成功消除了未学习客户端的贡献。
原文摘要 · Abstract (English)
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 rounds, followed by benign fine-tuning, enables fast removal of malicious effects and stable recovery. Experiments on three canonical datasets show that FAUN achieves recovery comparable to retraining while requiring far fewer rounds and reduces attack success rates to near zero, confirming FAUN successfully eliminates the contributions of unlearned clients.
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