A participant selection based vehicle platoon asynchronous federated learning framework

In the vehicular network, federated learning is an emerging paradigm to train deep learning models safely. However, the non-identically independent distributed data collected by intelligent connected vehicles, expensive training overhead, and the existence of malicious participants significantly affect the efficiency of training and constrain the development of federated learning in the vehicular network. To address the above issues, this paper proposes a participant selection-based asynchronous federated learning framework for an intelligent connected vehicle platoon. We proposed a platoon-based asynchronous federated learning framework that effectively improves training efficiency through data share and asynchronous optimization. Also, We designed a signaling game-based participant selection strategy that accurately identifies the potentially malicious participants by analyzing the Perfect Bayesian Equilibrium. The comparison experiment result shows the training model using our proposed scheme converges faster than the baseline scheme and gains approximately 10% higher accuracy.