Cross-Trust Domain Collaborative Federated Learning for IoV via Multi-Group Fully Homomorphic Encryption

Abstract

With the rapid evolution of 5G communications, artificial intelligence, and new energy technologies, intelligent driving has become a pivotal component of modern transportation infrastructure. However, the openness and inherent complexity of the Internet of Vehicles (IoV) pose significant challenges to data privacy and security. While Federated Learning (FL) facilitates collaborative training of data models without requiring raw data to leave local devices, its centralized aggregation architecture struggles to address privacy concerns in cross-trust domain collaborations and fails to effectively handle the challenges posed by vehicle node mobility. In this paper, we investigate cross-trust domain collaborative federated learning within the IoV and construct a privacy-preserving Cloud-Edge-End architecture based on Fully Homomorphic Encryption (FHE). To address the dynamic nature of vehicle nodes, we propose a hybrid privacy-preserving scheme that integrates Threshold Fully Homomorphic Encryption (ThFHE) and Multi-Key Fully Homomorphic Encryption (MKFHE). The experimental results indicate that the proposed collaborative architecture is theoretically reasonable. In addition, the scheme effectively achieves collaborative model training while maintaining security at the privacy protection level and supporting seamless vehicle mobility.