FLIGHT:Federated Learning with IRS for Grouped Heterogeneous Training  

作　　者：Tong Yin Lixin Li Donghui Ma Wensheng Lin Junli Liang Zhu Han 

机构地区：[1]School of Electronics and Information,Northwestern Poly technical University,Xi'an 710129,China [2]Department of Electrical and Computer Engineering at the University of Houston,Houston,TX 77004,USA,and also with the Department of Computer Science and Engineering,Kyung Hee University,Seoul 446-701,South Korea

出　　处：《Journal of Communications and Information Networks》2022年第2期135-144,共10页通信与信息网络学报（英文）

基　　金：National Natural Sci-ence Foundation of China(NSFC)(62001387);Shang-hai Academy of Spaceflight Technology(SAST)(SAST2020124);NSF(CNS-2107216);NSF(CNS-2128368)。

摘　　要：In recent years,federated learning(FL)has played an important role in private data-sensitive scenarios to perform learning tasks collectively without data exchange.However,due to the centralized model aggregation for heterogeneous devices in FL,the last updated model after local training delays the convergence,which increases the economic cost and dampens clients’motivations for participating in FL.In addition,with the rapid development and application of intelligent reflecting surface(IRS)in the next-generation wireless communication,IRS has proven to be one effective way to enhance the communication quality.In this paper,we propose a framework of federated learning with IRS for grouped heterogeneous training(FLIGHT)to reduce the latency caused by the heterogeneous communication and computation of the clients.Specifically,we formulate a cost function and a greedy-based grouping strategy,which divides the clients into several groups to accelerate the convergence of the FL model.The simulation results verify the effectiveness of FLIGHT for accelerating the convergence of FL with heterogeneous clients.Besides the exemplified linear regression(LR)model and convolutional neural network(CNN),FLIGHT is also applicable to other learning models.

关 键 词：federated learning decentralized aggrega-tion intelligent reflecting surfaces grouped learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]