UAV Deployment Optimization in Multi-UAV-Aided MEC Systems Using Federated Deep Reinforcement Learning

Abstract

Automatic aerial vehicle (UAV) aided Mobile Edge Computing (MEC) has emerged as a promising technique to offer computing support for high-mobility and high-demand mobile devices (MDs). However, due to the dynamic scale of UAVs and MDs as well as their changeable resource availability and demands, it is very challenging to quickly make suitable UAV deployment plans for satisfying the real-time requirements. Existing solutions commonly adopt the centralized decision-making manner based on the global information, which leads to poor scalability, excessive search time, and repeated training costs. To address these important challenges, we propose a novel Federated deep Reinforcement learning based UAV Deployment optimization method (FRUD) for multi-UAV-aided MEC systems, aiming to minimize the average task response time via optimizing the real-time deployment locations of large-scale UAVs. In FRUD, each UAV independently conducts the deployment decision-making based on the local information of runtime environments rather than using the global information. Next, through feedback control and multi-UAV cooperation, an effective UAV deployment plan can be gradually formed. Simulation and real-world testbed evaluation results show that the proposed FRUD well handles the UAV deployment problem in large-scale and dynamic multi-UAV-aided MEC systems and outperforms the state-of-art methods.