WFLTree: A Spanning Tree Construction for Federated Learning in Wireless Networks

作     者：Li, Shuo Zheng, Yanwei Zou, Yifei 

作者机构：Shaanxi Normal Univ Sch Math & Stat Xian 710062 Shaanxi Peoples R China Changji Univ Sch Math & Data Sci Changji 831100 Xinjiang Peoples R China Shandong Univ Sch Comp Sci & Technol Qingdao 266237 Shandong Peoples R China 

出 版 物：《JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS》 (电路、系统与计算机杂志)

年 卷 期：2023年第32卷第13期

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：University Scientific Research Program Foundation of Xinjiang Province [XJEDU2021I025] 

主　　题：Federated learning edge intelligence wireless networks distributed computing 

摘      要：Nowadays, more and more federated learning algorithms have been implemented in edge computing, to provide various customized services for mobile users, which has strongly supported the rapid development of edge intelligence. However, most of them are designed relying on the reliable device-to-device communications, which is not a realistic assumption in the wireless environment. This paper considers a realistic aggregation problem for federated learning in a single-hop wireless network, in which the parameters of machine learning models are aggregated from the learning agents to a parameter server via a wireless channel with physical interference constraint. Assuming that all the learning agents and the parameter server are within a distance gamma from each other, we show that it is possible to construct a spanning tree to connect all the learning agents to the parameter server for federated learning within O(log gamma) time steps. After the spanning tree is constructed, it only takes O(log gamma) time steps to aggregate all the training parameters from the learning agents to the parameter server. Thus, the server can update its machine learning model once according to the aggregated results. Theoretical analyses and numerical simulations are conducted to show the performance of our algorithm.