Mobility-Aware Reputation-Based Hierarchical Federated Learning for Internet of Vehicles

With the rapid development of the Internet of Vehicles (IoV), mobile vehicle clients can employ Federated Learning (FL) to collaboratively train an AI model in a distributed manner, thereby unlocking the full potential of IoV. However, due to the high mobility of vehicles, they may move out the coverage of edge server before completing local model training, making it difficult to upload their local models for aggregation on time. Furthermore, lazy vehicles with low-quality data or non-action contribute minimally to the global model while consuming system resources. These factors severely degrade the robustness and accuracy of FL. To mitigate the adverse effects, it's essential for the success of FL systems to select suitable vehicle clients based on the data quality and mobility. In this paper, we propose a mobility-aware reputation-based vehicle client selection scheme (MoReCS), where edge servers in a hierarchical federated learning (HFL) architecture dynamically select suitable vehicle clients based on real-time mobility information and reputation. Experimental results in mobile scenarios demonstrate that MoReCS effectively reduces the impact of vehicle mobility on model training and further enhances accuracy through the integration of a reputation mechanism.