Resource Allocation and Collaborative Offloading in Multi-UAV-Assisted IoV With Federated Deep Reinforcement Learning

In Internet of Vehicles (IoV), unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) can improve the system performance and communication range of intelligent transportation systems (ITSs). However, the resource allocation and computation offloading in UAVs-assisted IoV systems still face huge challenges due to the growing number of vehicle terminals (VTs), potential privacy leakage, and inefficient problem-solving. Existing solutions cannot adapt to such dynamic multi-UAV scenarios and meet the real-time requirements of VTs. To address these challenges, we propose RACOMU, a novel resource allocation and collaborative offloading framework for multi-UAV-assisted IoV. First, we introduce the convex optimization theory to decouple the original problem and then obtain the near-optimal allocation of transmission power and computing resources by solving the Karush-Kuhn–Tucker (KKT) condition. Next, we design a new collaborative offloading strategy with federated deep reinforcement learning (FDRL), where the offloading requests from VTs are processed in a distributed manner to approach the global optimum while preserving data privacy. Extensive experiments verify the effectiveness of the proposed RACOMU. Compared to benchmark methods, RACOMU achieves better performance in terms of task processing latency, decision-making time, and load balancing degree under various scenarios.