Affiliation:
1. Mashang Consumer Finance Co., Ltd., Chongqing 401121, China
2. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Abstract
Due to the wide connection range and open communication environment of internet of vehicle (IoV) devices, they are susceptible to Byzantine attacks and privacy inference attacks, resulting in security and privacy issues in IoV federated learning. Therefore, there is an urgent need to study IoV federated learning methods with privacy protection. However, the heterogeneity and resource limitations of IoV devices pose significant challenges to the aggregation of federated learning model parameters. Therefore, this paper proposes an asynchronous robust aggregation method with privacy protection for federated learning in IoVs. Firstly, we design an asynchronous grouping robust aggregation algorithm based on delay perception, combines intra-group truth estimation with inter-group delay aggregation, and alleviates the impact of stragglers and Byzantine attackers. Then, we design a communication-efficient and security enhanced aggregation protocol based on homomorphic encryption, to achieve asynchronous group robust aggregation while protecting data privacy and reducing communication overhead. Finally, the simulation results indicate that the proposed scheme could achieve a maximum improvement of 41.6% in model accuracy compared to the baseline, which effectively enhances the training efficiency of the model while providing resistance to Byzantine attacks and privacy inference attacks.
Funder
Chongqing Key projects for Technological Innovation and Application Development
National Natural Science Foundation of China
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