Article: A data compliance sharing algorithm for intelligent connected vehicles empowered by federated learning Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.36 pp.42 - 57 Abstract: With the rapid development of intelligent connected vehicle (ICV) technology, massive amounts of vehicle data have become an important resource for advancing intelligent mobility and autonomous driving technologies. However, the sharing of these data involves significant privacy leakage risks and compliance challenges, especially in multi-party collaboration scenarios. To this end, this paper proposes a federated learning (FL) framework integrating differential privacy and Paillier homomorphic encryption for intelligent connected vehicle (ICV) data sharing. The architecture integrates differential privacy (DP) and homomorphic encryption (HE) through four functional layers. The four-layer architecture achieves 93.5% model accuracy with 79% lower privacy leakage risk (0.21 vs. 0.98 baseline) on 50,000 driving scenarios. Momentum-accelerated averaging and 8-bit gradient quantisation reduce bandwidth consumption by 62%. Experimental validation demonstrates superior privacy-utility balance compared to standalone DP (0.6 risk) and HE (0.5 risk) implementations. Inderscience Publishers - linking academia, business and industry through research

Title: A data compliance sharing algorithm for intelligent connected vehicles empowered by federated learning

Authors: Bei Zhou

Addresses: School of Economic Law, Southwest University of Political Science and Law, Chongqing, 401120, China

Abstract: With the rapid development of intelligent connected vehicle (ICV) technology, massive amounts of vehicle data have become an important resource for advancing intelligent mobility and autonomous driving technologies. However, the sharing of these data involves significant privacy leakage risks and compliance challenges, especially in multi-party collaboration scenarios. To this end, this paper proposes a federated learning (FL) framework integrating differential privacy and Paillier homomorphic encryption for intelligent connected vehicle (ICV) data sharing. The architecture integrates differential privacy (DP) and homomorphic encryption (HE) through four functional layers. The four-layer architecture achieves 93.5% model accuracy with 79% lower privacy leakage risk (0.21 vs. 0.98 baseline) on 50,000 driving scenarios. Momentum-accelerated averaging and 8-bit gradient quantisation reduce bandwidth consumption by 62%. Experimental validation demonstrates superior privacy-utility balance compared to standalone DP (0.6 risk) and HE (0.5 risk) implementations.

Keywords: intelligent connected vehicle; ICV; federated learning; FL; data compliance sharing; privacy protection.

DOI: 10.1504/IJICT.2025.149054

International Journal of Information and Communication Technology, 2025 Vol.26 No.36, pp.42 - 57

Received: 02 Jul 2025
Accepted: 16 Aug 2025
Published online: 10 Oct 2025 *

Title: A data compliance sharing algorithm for intelligent connected vehicles empowered by federated learning

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