Practical Privacy-Preserving Federated Learning in Vehicular Fog Computing
Yiran Li, Hongwei Li, Guowen Xu, Tao Xiang, Rongxing Lu · IEEE Transactions on Vehicular Technology · 2022
Benefitting from the outstanding capabilities of intelligent controlling and prediction, federated learning (FL) has been widely applied in Internet of Vehicle (IoV). However, applying FL into fog-computing-based IoV still suffers from two crucial problems: (i) how to achieve the privacy-preserving FL under the flexible architecture of fog computing with no assistance of cloud server, and (ii) how to guarantee the privacy-preserving FL to perform with high efficiency and low overhead in fog-computing settings. For addressing the above issues, we propose a practical framework, namedGalaxy, the first of its kind in the regime of privacy-preserving FL under the setting of non-cloud-assisted fog computing. Based on the secure multi-party computation (MPC) technology, our framework satisfies the$\boldsymbol{(T,N)}$-threshold property, permitting$\boldsymbol{N}$(a scalable number) fog nodes to cooperate with multiple users for implementing privacy-preserving FL, while resisting the collusion up to$\boldsymbol{T}-\boldsymbol{1}$fog nodes, and being robust to at most$\boldsymbol{N}-\boldsymbol{T}$fog nodes simultaneously dropping out. Besides, considering the practical scenario that low-quality data may negatively impair the FL model convergence, our scheme can handle users’ low-quality data while protecting all user-related information under our secure framework. Based on the above superior properties, our scheme can perform with high scalability, high processing efficiency, and low resource overhead, being practical for fog-computing-based IoV. Extensive experiment results demonstrate our scheme with high-level performance.