Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design

doi:10.1109/ICCWorkshops53468.2022.9814684

	Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design
	Huang, Shaoming1,2,3 ; Zhang, Pengfei1,2,3 ; Mao, Yijie1 ; Lian, Lixiang1 ; Wu, Youlong1 ; Shi, Yuanming1
	2022
会议录名称	2022 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS, ICC WORKSHOPS 2022
ISSN	2164-7038
页码	794-799
发表状态	已发表
DOI	10.1109/ICCWorkshops53468.2022.9814684
摘要	Federated learning (FL) is recognized as a key enabling technology to support distributed artificial intelligence (AI) services in future 6G. By supporting decentralized data training and collaborative model training among devices, FL inherently tames privacy leakage and reduces transmission costs. Whereas, the performance of the wireless FL is typically restricted by the communication latency. Multiple-input multiple-output (MIMO) technique is one promising solution to build up a communication-efficient edge FL system with limited radio resources. In this paper, we propose a novel joint device scheduling and receive beamforming design approach to reduce the FL convergence gap over shared wireless MIMO networks. Specifically, we theoretically establish the convergence analysis of the FL process, and then apply the proposed device scheduling policy to maximize the number of weighted devices under the FL system latency and sum power constraints. Numerical results verify the theoretical analysis of the FL convergence and exhibit the appealing learning performance of the proposed approach. © 2022 IEEE.
关键词	MIMO systems Scheduling Decentralised Device scheduling Distributed Artificial Intelligence Enabling technologies Federated learning system Intelligence services Learning convergence Multiple inputs Multiple outputs Output network
会议名称	2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
会议地点	Seoul, Korea, Republic of
会议日期	May 16, 2022 - May 20, 2022
URL	查看原文
收录类别	CPCI-S ; EI ; CPCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000848467200133
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20223012414054
EI主题词	Beamforming
EI分类号	711.2 Electromagnetic Waves in Relation to Various Structures - 912.2 Management
原始文献类型	Conference article (CA)
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/211722
专题	信息科学与技术学院_PI研究组_廉黎祥组信息科学与技术学院_PI研究组_吴幼龙组信息科学与技术学院_PI研究组_石远明组信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_毛奕婕组
通讯作者	Huang, Shaoming; Zhang, Pengfei; Mao, Yijie; Lian, Lixiang; Wu, Youlong; Shi, Yuanming
作者单位	1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Huang, Shaoming,Zhang, Pengfei,Mao, Yijie,et al. Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2022:794-799.