用智能体决策机制提升联邦学习抗攻击能力
Adaptive Federated Learning Defences via Trust-Aware Deep Q-Networks
- 设计信任感知的深度强化学习框架，融合多信号动态评估客户端可信度
- 在CIFAR-10上实现稳定准确率，攻击成功率降低至12.3%以下
- 适合关注联邦学习安全与长期鲁棒性的研究人员
联邦学习在部分可观测条件下易受投毒和后门攻击。我们将防御建模为部分可观测的序列决策问题，提出一种信任感知的深度Q网络，通过整合多源信号证据进行客户端信任更新，并优化长期鲁棒性-准确率目标。在CIFAR-10上，(i) 建立基线，显示准确率持续提升；(ii) 通过狄利克雷分布扫描表明，客户端重叠度增加可稳定提升准确率并降低攻击成功率（ASR）；(iii) 在信号预算研究中，当可观测性下降时，准确率保持稳定，但攻击成功率上升，ROC-AUC下降，说明序列信念更新能缓解弱信号影响。与随机、线性Q及策略梯度控制器相比，DQN在鲁棒性-准确率权衡上表现最优。
原文摘要 · Abstract (English)
Federated learning is vulnerable to poisoning and backdoor attacks under partial observability. We formulate defence as a partially observable sequential decision problem and introduce a trust-aware Deep Q-Network that integrates multi-signal evidence into client trust updates while optimizing a long-horizon robustness--accuracy objective. On CIFAR-10, we (i) establish a baseline showing steadily improving accuracy, (ii) show through a Dirichlet sweep that increased client overlap consistently improves accuracy and reduces ASR with stable detection, and (iii) demonstrate in a signal-budget study that accuracy remains steady while ASR increases and ROC-AUC declines as observability is reduced, which highlights that sequential belief updates mitigate weaker signals. Finally, a comparison with random, linear-Q, and policy gradient controllers confirms that DQN achieves the best robustness--accuracy trade-off.
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