A Dynamic Clustering-Based Hierarchical Federated Learning Scheme in Internet of Vehicles

Abstract

Federated learning (FL) can significantly empower the Internet of Vehicles (IoV) by utilizing in-vehicle communications resources and data to construct high-quality models. In this letter, focusing on improving the training efficiency of FL in the IoV scenario with heterogeneous in-vehicle communications resources and datasets, we propose a dynamic clustering-based hierarchical FL scheme. Specifically, dynamic clustering can reduce the adverse effects of heterogeneity and save communication cost during training, while hierarchical aggregation that combines synchronous aggregation within each cluster and asynchronous aggregation among clusters promotes global convergence. The simulation results show that the proposed scheme accelerates the convergence rate by 20.1% – 57.5%, reduces the communication cost by 9.2% – 63.9%, and has better robustness compared with current typical FL schemes.