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

• URL: http://arxiv.org/abs/2510.01261v1
• Date: Thu, 25 Sep 2025 13:30:09 GMT
• Title: Adaptive Federated Learning Defences via Trust-Aware Deep Q-Networks
• Abstract summary: Federated learning is vulnerable to poisoning and backdoor attacks under partial observability.<n>We introduce a trust-aware Deep Q-Network that integrates multi-signal evidence into client trust updates.
• Score: 1.5374297736981706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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