Age of Information Based Client Selection for Wireless Federated Learning With Diversified Learning Capabilities

doi:10.1109/TMC.2024.3450549

	Age of Information Based Client Selection for Wireless Federated Learning With Diversified Learning Capabilities
	Dong, Liran 1,2,3; Zhou, Yiqing 1,2,3; Liu, Ling 1,2,3; Qi, Yanli 1,2,3; Zhang, Yu 1,2,3
	2024-12-01
发表期刊	IEEE TRANSACTIONS ON MOBILE COMPUTING
ISSN	1536-1233
卷号	23 期号:12 页码:14934-14945
摘要	Federated Learning (FL) empowers wireless intelligent applications, by leveraging distributed data of edge clients for training without compromising privacy. Client selection is inevitable in FL, since clients have diversified learning capabilities arising from heterogeneous computing and communication resources. Existing methods like fair-selection and dropping-straggler are either inefficient or unfair (resulting in a less effective trained model). Therefore, we propose FedAoI, an Age-of-Information (AoI) based client selection policy. FedAoI ensures fairness by allowing all clients, including stragglers, to submit their model updates while maintaining high training efficiency by keeping round completion times short. This trade-off is achieved by minimizing Peak-AoI (PAoI), the interval between a client's consecutive participations. An optimization problem is formulated by minimizing the Expected-Weighted-Sum-of-PAoI. This NP-hard problem is addressed with a two-step sub-optimal algorithm, PriorS. It first calculates client priority in a round using Lyapunov optimization and then selects the highest-priority clients through G-FPFC (Greedy minimization of the round weighted-sum-of-PAoI with First-Priority-First-Considered). Simulation results demonstrate that, compared to fair-selection, FedAoI improves average efficiency by 83.8% and achieves an average model accuracy of 97.3% (or at the cost of averaging 2.7% degradation in model accuracy). Compared to dropping-straggler, FedAoI reduces the average model accuracy degradation from 9.5% to 2.7%.
关键词	Training Computational modeling Servers Data models Wireless communication Mobile computing Accuracy Federated learning (FL) age of information (AoI) client selection fairness scheduling
DOI	10.1109/TMC.2024.3450549
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[U21A20449] ; National Key Research and Development Program of China[2021YFA1000500] ; National Key Research and Development Program of China[2021YFA1000501] ; CAS Project for Young Scientists in Basic Research[YSBR-035]
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:001359244600222
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41109
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhou, Yiqing
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China 2.Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Dong, Liran,Zhou, Yiqing,Liu, Ling,et al. Age of Information Based Client Selection for Wireless Federated Learning With Diversified Learning Capabilities[J]. IEEE TRANSACTIONS ON MOBILE COMPUTING,2024,23(12):14934-14945.
APA	Dong, Liran,Zhou, Yiqing,Liu, Ling,Qi, Yanli,&Zhang, Yu.(2024).Age of Information Based Client Selection for Wireless Federated Learning With Diversified Learning Capabilities.IEEE TRANSACTIONS ON MOBILE COMPUTING,23(12),14934-14945.
MLA	Dong, Liran,et al."Age of Information Based Client Selection for Wireless Federated Learning With Diversified Learning Capabilities".IEEE TRANSACTIONS ON MOBILE COMPUTING 23.12(2024):14934-14945.