P2P Federated Learning Based on Node Segmentation with Privacy Protection for IoV

Abstract

The current usage of federated learning in applications relies on the existence of servers. To address the inability to conduct federated learning for IoV (Internet of Vehicles) applications in serverless areas, a P2P (peer-to-peer) architecture for federated learning is proposed in this paper. Following node segmentation based on limited subgraph diameters, an edge aggregation mode is employed to propagate models inwardly, and a mode for propagating the model inward to the C-node (center node) while aggregating is proposed. Simultaneously, a personalized differential privacy scheme was designed under this architecture. Through experimentation and verification, the approach proposed in this paper demonstrates the combination of both security and usability.