How to Tame Mobility in Federated Learning Over Mobile Networks?

doi:10.1109/TWC.2023.3272920

	How to Tame Mobility in Federated Learning Over Mobile Networks?
	Peng, Yan 1,2,3; Tang, Xiaogang 4; Zhou, Yiqing 1,2,3; Hou, Yuenan 5; Li, Jintao 1,2,3; Qi, Yanli 1,2,3; Liu, Ling 1,2,3; Lin, Hai 4
	2023-12-01
发表期刊	IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
ISSN	1536-1276
卷号	22 期号:12 页码:9640-9657
摘要	Federated learning (FL) over mobile networks has attracted intensive attention recently. User mobility is a fundamental feature of mobile networks, which leads to dynamic network topology and wireless connectivity losses. As such, user mobility is usually considered a "trouble maker" and a great challenge to FL over mobile networks. Interestingly, we found that small user mobility can positively contribute to improving FL performance. This is because the total dataset size and the data diversity that the FL can utilize are increased by user mobility. Based on this observation, we aim to tame and exploit mobility instead of treating it as a hostile "trouble maker". To this end, we first investigate how the FL performance changes with user mobility theoretically by jointly taking into account the positive and negative aspects of mobility. Specifically, a closed-form expression to quantify the impact of mobility on the FL loss is derived, which explains when negative or positive aspects of mobility dominate the FL performance. Next, a joint FL and communication optimization problem is formulated based on theoretical analyses to minimize the FL loss function by optimizing wireless resource allocation. Finally, we propose a two-step optimization algorithm to solve the formulated problem. The simulation results verify the theoretical analyses. It is also shown that the proposed method can significantly enhance learning performance considering users with high mobility. When the average velocity is larger than 150 km/h, the proposed method achieves more than 80% accuracy in the MNIST dataset, while the existing methods may fail during training.
关键词	Federated learning user mobility resource allocation convergence analysis
DOI	10.1109/TWC.2023.3272920
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001128031700075
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38829
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Tang, Xiaogang; Zhou, Yiqing
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 4.Space Engn Univ, Sch Aerosp Informat, Beijing 100015, Peoples R China 5.Shanghai AI Lab, Shanghai 200232, Peoples R China
推荐引用方式 GB/T 7714	Peng, Yan,Tang, Xiaogang,Zhou, Yiqing,et al. How to Tame Mobility in Federated Learning Over Mobile Networks?[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,2023,22(12):9640-9657.
APA	Peng, Yan.,Tang, Xiaogang.,Zhou, Yiqing.,Hou, Yuenan.,Li, Jintao.,...&Lin, Hai.(2023).How to Tame Mobility in Federated Learning Over Mobile Networks?.IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,22(12),9640-9657.
MLA	Peng, Yan,et al."How to Tame Mobility in Federated Learning Over Mobile Networks?".IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 22.12(2023):9640-9657.