Hierarchical Federated Edge Learning over Space-Air-Ground Integrated Networks

doi:10.1109/GCWkshps58843.2023.10464713

	Hierarchical Federated Edge Learning over Space-Air-Ground Integrated Networks
	Yiji, Wang1 ; Jingyang, Zhu1 ; Yijie, Mao1 ; Dingzhu, Wen1 ; Xiaohua, Tian 2; Yuanming, Shi1
	2023-12-08
会议录名称	2023 IEEE GLOBECOM WORKSHOPS (GC WKSHPS)
页码	190-196
发表状态	已发表
DOI	10.1109/GCWkshps58843.2023.10464713
摘要	Federated learning (FL), as a key application scenario to support distributed artificial intelligence (AI) services, allows an amount of edge devices to train a global model in tandem. In addition, space-air-ground (SAG) integrated networks are envisioned to provide AI services for remote areas in the next generation of wireless communication system, which provides new opportunities for the integration of SAG and FL. Whereas, the performance of the SAG-FL is limited by significant system latency, high energy consumption, and slow convergence rate. In this paper, we consider a SAG hierarchical FL network, where a low-earth-orbit (LEO) satellite serves as the cloud server and multiple unmanned aerial vehicles (UAVs) act as the edge nodes covering multiple cells of edge devices. Specifically, we provide the convergence analysis of the SAG-FL algorithm, and then alternatively optimize the device scheduling policy and UAVs' trajectory to minimize the system delay and energy consumption between two global aggregations. Simulation results illustrate that the proposed dynamic device scheduling policy is much more time-saving and energy-efficient than the static one. © 2023 IEEE.
关键词	Performance evaluation Energy consumption Simulation Heuristic algorithms Low earth orbit satellites Dynamic scheduling Delays
会议名称	2023 IEEE Globecom Workshops, GC Wkshps 2023
会议地点	Kuala Lumpur, Malaysia
会议日期	4-8 Dec. 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20241615926467
原始文献类型	Conference article (CA)
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/345932
专题	信息科学与技术学院_PI研究组_毛奕婕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_博士生信息科学与技术学院_PI研究组_文鼎柱组
作者单位	1.ShanghaiTech University, School of Information Science and Technology 2.Shanghai Jiao Tong University, School of Electronic Information and Electrical Engineering
第一作者单位	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	Yiji, Wang,Jingyang, Zhu,Yijie, Mao,et al. Hierarchical Federated Edge Learning over Space-Air-Ground Integrated Networks[C]:Institute of Electrical and Electronics Engineers Inc.,2023:190-196.