标题： 基于信任感知深度Q网络的自适应联邦学习防御方法 显示英文标题

标题： Adaptive Federated Learning Defences via Trust-Aware Deep Q-Networks

摘要： 联邦学习在部分可观测性下容易受到中毒攻击和后门攻击。 我们将防御建模为一个部分可观测的序列决策问题，并引入一种信任感知的深度Q网络，该网络在优化长期鲁棒性-准确性目标的同时，将多信号证据整合到客户端信任更新中。 在CIFAR-10上，我们（i）建立了一个基线，显示出准确率持续提高，（ii）通过狄利克雷扫描表明，增加的客户端重叠一致地提高了准确率并减少了ASR，同时保持稳定的检测，（iii）在信号预算研究中展示了当可观测性降低时，准确率保持稳定而ASR增加，ROC-AUC下降，这突显了序列信念更新可以缓解较弱的信号。 最后，与随机、线性-Q和策略梯度控制器的比较确认了DQN实现了最佳的鲁棒性-准确性权衡。 显示英文摘要

摘要： 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.