CSA_FedVeh: Cluster-Based Semi-asynchronous Federated Learning Framework for Internet of Vehicles

In Internet of Vehicles (IoV) system, Federated Learning (FL) is a novel distributed approach to processing real-time vehicle data that enables training of shared learning models while ensuring data privacy. However, existing FL still face numerous challenges in IoV. Firstly, the fast convergence with FL models is difficult to achieve due to the high mobility of vehicles and the non-independent identical distribution (Non-IID) among data collected by vehicles. Moreover, the parameter aggregation process of FL incurs significant communication overhead, and the varying computing power of vehicles results in the straggler. To address these issues, this paper proposes a Cluster-based Semi-Asynchronous Federated Learning framework for IoV (CSA_FedVeh). Specifically, we propose a Space-Time and Weight DBSCAN density clustering algorithm (STW-DBSCAN) that relies on both the space-time location and model weight similarities of vehicles. Clustering of vehicles can alleviate the impact of Non-IID data, and the joint training of data vehicles can reduce resource consumption and mitigate the straggler effect. In addition, we adopt a semi-asynchronous FL aggregation mechanism to reduce communication time and improve FL efficiency. Experimental results show that compared with baselines under Non-IID datasets, CSA_FedVeh can reduce the running time by about 24.6% to 60.2%, and reduce communication consumption by 3.4% to 62.07% on MNIST dataset and 1.01% to 68.6% on GTSRD dataset.