Privacy-Preserving Byzantine-Robust Federated Learning via Deep Reinforcement Learning in Vehicular Networks
Yanghe Pan, Zhou Su, Yuntao Wang, Jinhao Zhou, Mohamed M. E. A. Mahmoud · IEEE Transactions on Vehicular Technology · 2025
Federated learning (FL), as a transformative approach in vehicular networks, enables collaborative model training without exposure of vehicle users' local data. However, vehicular FL services are susceptible to Byzantine attacks, since malicious vehicles can upload false local updates to degrade the performance of the global model. Additionally, the honest-but-curious mobile edge computing (MEC) nodes may attempt to extract sensitive information from the shared updates via inference or inversion attacks. In this paper, we propose DRL-PBFL, a privacy-preserving Byzantine-robust FL scheme in vehicular networks that tolerates Byzantine vehicles and the honest-but-curious MEC node during the training process. Specifically, the deep reinforcement learning (DRL) technique is leveraged to maintain the robustness of FL against Byzantine vehicles, and a novel secure aggregation algorithm is designed to prevent curious inferences from the honest-but-curious MEC node. A performance-based weighting aggregation policy optimized by a deep deterministic policy gradients (DDPG) component is also devised to aggregate local updates. As it is challenging to directly integrate weighting aggregation policy with general pseudo-random masking methods for Byzantine resistance, the Lagrange interpolation is utilized to generate local perturbations to defend against the honest-but-curious MEC node and eliminate the weighted perturbations after secure aggregation. Notably, DRL-PBFL effectively achieves Byzantine robustness in FL even though the vehicles' data is not independently and identically distributed (non-IID). Experiments on three benchmark datasets with different adversarial settings show that DRL-PBFL effectively improves the global model accuracy under tailored Byzantine attacks compared with state-of-the-art schemes.