Energy–Latency–Accuracy Trade-Off in UAV-Assisted VECNs: A Robust Optimization Approach Under Channel Uncertainty

Highlights: What are the main findings? We develop a unmanned aerial vehicle (UAV)-assisted federated learning (FL) framework for vehicular edge computing networks (VECNs), which jointly tackles FL training accuracy and resource allocation challenges. To address the channel uncertainties induced by vehicular mobility, we propose an asynchronous parallel deep deterministic policy gradient (APDDPG) algorithm combined with a reputation-based client selection scheme to solve the joint optimization problem. What are the implications of the main findings? The proposed UAV-assisted FL framework not only enhances VECNs with seamless connectivity and improved efficiency but also enables timely distributed decision-making and supports large-scale service provisioning. The APDDPG algorithm and client selection scheme form the core technologies of the proposed framework, advancing VECNs toward more robust transmission and intelligent collaborative operation. Federated learning (FL)-based vehicular edge computing networks (VECNs) are emerging as a key enabler of intelligent transportation systems, as their privacy-preserving and distributed architecture can safeguard vehicle data while reducing latency and energy consumption. However, conventional roadside units face processing bottlenecks in dense traffic and at the network edge, motivating the adoption of unmanned aerial vehicle (UAV)-assisted VECNs. To address this challenge, this paper proposes a UAV-assisted VECN framework with FL, aiming to improve model accuracy while minimizing latency and energy consumption during computation and transmission. Specifically, a reputation-based client selection mechanism is introduced to enhance the accuracy and reliability of federated aggregation. Furthermore, to address the channel dynamics induced by high vehicle mobility, we design a robust reinforcement learning-based resource allocation scheme. In particular, an asynchronous parallel deep deterministic policy gradient (APDDPG) algorithm is developed to adaptively allocate computation and communication resources in response to real-time channel states and task demands. To ensure consistency with real vehicular communication environments, field experiments were conducted and the obtained measurements were used as simulation parameters to analyze the proposed algorithm. Compared with state-of-the-art algorithms, the developed APDDPG algorithm achieves 20% faster convergence, 9% lower energy consumption, a FL accuracy of 95.8%, and the most robust standard deviation under varying channel conditions.