Optimization for Efficient Federated Learning: Joint Scheduling in Vehicular Edge Computing Networks

In the context of rapid urban informatization, numerous vehicular devices have undertaken the responsibilities of local storage and data processing, with Federated Learning (FL) assuming a pivotal role within Vehicular Edge Computing Networks (VECNs). However, disparities in data quality and resources among vehicles may pose challenges to the efficiency of FL. To this end, we investigate the client selection and resource allocation issues specific to Unmanned Aerial Vehicle (UAV)-assisted vehicles within the domain of FL. Firstly, we construct a dynamic interactive reputation model where UAVs evaluate and select client vehicles based on factors like performance and capability, effectively filtering out high-quality data sources and enhancing the system's ability to resist malicious node attacks. Secondly, we formulate a joint optimization problem to design a scheduling strategy that efficiently manages computational resources and communication capabilities, thus controlling latency and reducing energy consumption resulting from local model training. Additionally, we propose an asynchronous parallel Deep Deterministic Policy Gradient (APDDPG) algorithm with shared experience replay, aimed at enhancing the stability of global model convergence. Simulation results reveal that our proposed model and algorithm can more effectively resist attacks from malicious nodes and more fully utilize resources compared to other approaches, ultimately achieving efficient FL.