Client selection and resource scheduling in reliable federated learning for UAV-assisted vehicular networks

作     者：Zhao, Hongbo Geng, Liwei Feng, Wenquan Zhou, Changming 

作者机构：Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China 

出 版 物：《CHINESE JOURNAL OF AERONAUTICS》 (中国航空学报（英文版）)

年 卷 期：2024年第37卷第9期

页      面：328-346页

核心收录：

学科分类：08[工学] 0825[工学-航空宇航科学与技术] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61901015  62301017] 

主　　题：Federated learning Vehicular edge computing Resource management Reinforcement learning Optimization techniques EFFICIENT ALLOCATION COMMUNICATION OPTIMIZATION NAVIGATION INTERNET 

摘      要：Federated Learning (FL), a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks (VECN), allows a distributed approach to train datasets of nodes locally, e.g., for mobile vehicles, and exchanges model parameters to obtain an accurate model without raw data transmission. However, the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models. Moreover, the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources. In view of this, we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks (FLVN). We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles. Further, to fortify the FL reliability, we adopt the consortium blockchain to oversee the reputation information, which boasts tamper-proof and interference-resistant qualities. Finally, we formulate the resource scheduling problem by jointly optimizing the computation capability, the transmission power, and the number of local training rounds, aiming to minimize the cost of clients while guaranteeing accuracy. To this end, we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy. Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption. (c) 2024 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).