1. Practical Privacy-Preserving Federated Learning in Vehicular Fog Computing.
- Author
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Li, Yiran, Li, Hongwei, Xu, Guowen, Xiang, Tao, and Lu, Rongxing
- Subjects
INTELLIGENT control systems ,FOG ,SCALABILITY ,COLLUSION ,EDGE computing - Abstract
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, named Galaxy, 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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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