FedRGD: Risk-Guided Dynamic Defense against Federated Backdoors
Federated Learning (FL) is vulnerable to backdoor attacks, where adversaries can stealthily manipulate the global model. Most existing defense methods are developed under IID assumptions, an assumption that rarely holds in practice. In highly non-IID settings, heterogeneous data distributions across clients make it difficult to distinguish malicious updates from benign ones, particularly when benign clients exhibit atypical patterns due to minority-class data. To address this challenge, existing defenses operate at different levels of granularity. Coarse-grained methods perform client-level filtering, which often mistakenly excludes benign clients under non-IID conditions. Fine-grained methods instead analyze data at the sample level for more precise detection, but typically rely on explicit per-sample gradient analysis, leading to substantial memory and computational overhead. As a result, defending against backdoor attacks in non-IID environments involves a fundamental trade-off between robustness and computational efficiency. To address this challenge, we propose FedRGD, a federated risk-guided dynamic defense framework that enables efficient fine-grained protection. FedRGD maps sample-level risks into structured parameter masking without requiring explicit per-sample gradient storage. It combines feature inconsistency detection with lightweight masking and robust aggregation to achieve both accuracy and efficiency. Extensive experiments on CIFAR-10 and Fashion-MNIST demonstrate that FedRGD consistently reduces the attack success rate while maintaining high main-task accuracy, achieving a favorable security-utility balance with low computational overhead.