Mobility-Aware Semi-Asynchronous Federated Learning for Vehicular Networks

With the rapid development of intelligent transportation systems, vehicular networks (VNs) have become a key component for acquiring and transmitting data regarding vehicle movement and environmental conditions. Considering the privacy and security issues within VNs, Federated Learning (FL), as a distributed machine learning approach, offers a powerful tool that utilizes the onboard central processing units (CPUs) and local datasets of mobile Connected Vehicles (CVs) to train a global model. However, most existing FL implementations rely on synchronous training to ensure favorable convergence performance, but the mobility of CVs often leads to communication instability, which may result in significant straggler issues. Furthermore, CVs frequently leave the coverage area of the roadside unit (RSU) before successfully uploading their local models, thereby reducing the accuracy of the global model. To address the above problems, in this paper, we propose a communication-efficient semi-asynchronous FL (ESAFL) scheme to fit to VNs and enhance learning performance. Specifically, the RSU clusters CVs into different network layers according to their arrival order in each epoch and mitigates the straggler effect in FL by adjusting the participation frequency of CVs in various layers. Afterward, an Age of Information aggregation strategy is employed to balance the contributions of models transmitted by CVs with varying delays. Exploiting the MNIST and CIFAR10 datasets in simulations, we show that our proposed ESAFL achieves significant improvements in both the accuracy of the model and the convergence speed, indicating a more effective approach to FL in dynamic and heterogeneous environments.