Federated Deep Reinforcement Learning-Based Intelligent Dynamic Services in UAV-Assisted MEC
Peng Hou, Xiaohan Jiang, Zongshan Wang, Sen Liu, Zhihui Lu
Abstract
Unmanned aerial vehicles (UAVs)-assisted multiaccess edge computing (MEC) has emerged as a promising solution in B5G/6G networks. The high flexibility and seamless connectivity of UAVs make them well suited for providing enhanced communications coverage and efficient computing support. Particularly, in situations where ground facilities may be compromised or communication is unreliable. In this article, we study joint dynamic service switching and resource allocation for multiple UAVs in MEC network. We consider the heterogeneity of tasks and UAVs and model the dynamic service process of UAVs as a sequential decision problem based on the Markovian decision process. To enable dynamic and intelligent UAV service, we first propose a centralized dynamic service algorithm DDPG-based centralized (DDBC) based on deep reinforcement learning. However, given the training difficulties of the centralized algorithm, we propose a more promising distributed learning algorithm FLBF, which combines federated learning. We conduct extensive simulations to evaluate the effectiveness and advantages of the proposed algorithms. Our results show that DDBC and FLBF significantly reduce the system cost by 17.99%–35.72% and 12.30%–31.26%, respectively, compared to the comparative algorithms. Furthermore, FLBF can effectively improve the convergence speed with guaranteed learning performance, indicating its suitability for model training in UAV-assisted MEC networks.