A Lightweight Authentication and Privacy-Preserving Aggregation for Blockchain-Enabled Federated Learning in VANETs
Peng Liu, Qian He, Yi‐Ting Chen, Shan Jiang, Baokang Zhao, Xichan Wang
Abstract
Intelligent Transport Systems are designed to revolutionize the performance and efficiency of Vehicular Ad Hoc Networks (VANETs). Consumer electronics encompassing autonomous driving and route planning contribute to a better driving experience. Federated learning enables vehicle collaboration to train global models of intelligent transport without sharing local data for personalized consumer electronics. However, due to the dynamic network topology and unreliable open channels of VANETs, various potential risks undermine the credibility of establishing intermediate model parameters. To address these issues, we propose a lightweight authentication and privacy-preserving aggregation scheme for blockchain-enabled federated learning in VANETs(LPBFL). Specifically, we first construct a distributed secure authentication framework for blockchain federated learning that facilitates seamless authentication of mobile vehicles and security of model transmission. Subsequently, we design a lightweight three-party authentication key agreement based on chaotic map that establishes a session key for secure transmission of the intermediate model. In addition, we propose a continuous authentication adaptive model aggregation algorithms to ensure local model integrity while improving the quality of global models. Finally, security analysis and proofs show that LPBFL enhances the privacy and reliability of intermediate model parameters. Comprehensive experimental evaluations substantiate that the proposed LPBFL facilitates lightweight authentication while maintaining superior model accuracy.