FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

doi:10.1007/s11390-023-3009-0

	FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring
	Hu, Chun-Yu 1,2,3; Hu, Li-Sha 4; Yuan, Lin 1,2; Lu, Dian-Jie 3,5; Lyu, Lei 3,5; Chen, Yi-Qiang 6
	2023-09-01
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN	1000-9000
卷号	38 期号:5 页码:970-984
摘要	Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption. However, most wearable health data is distributed across different organizations, such as hospitals, research institutes, and companies, and can only be accessed by the owners of the data in compliance with data privacy regulations. The first challenge addressed in this paper is communicating in a privacy-preserving manner among different organizations. The second technical challenge is handling the dynamic expansion of the federation without model retraining. To address the first challenge, we propose a horizontal federated learning method called Federated Extremely Random Forest (FedERF). Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model performance. Based on FedERF, we present a federated incremental learning method called Federated Incremental Extremely Random Forest (FedIERF) to address the second technical challenge. FedIERF introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model incrementally. The experiments show that FedERF achieves comparable performance with non-federated methods, and FedIERF effectively addresses the dynamic expansion of the federation. This opens up opportunities for cooperation between different organizations in wearable health monitoring.
关键词	federated learning incremental learning random forest wearable health monitoring
DOI	10.1007/s11390-023-3009-0
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62002187] ; National Natural Science Foundation of China[62002189] ; National Natural Science Foundation of China[61972383] ; National Natural Science Foundation of China[61972237] ; National Natural Science Foundation of China[61976127] ; Science Research Project of Hebei Education Department of China[QN2023184]
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号	WOS:001114345700009
出版者	SPRINGER SINGAPORE PTE LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38456
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chen, Yi-Qiang
作者单位	1.Qilu Univ Technol, Shandong Acad Sci, Shandong Comp Sci Ctr, Natl Supercomp Ctr Jinan,Key Lab Comp Power Networ, Jinan 250353, Peoples R China 2.Shandong Fundamental Res Ctr Comp Sci, Shandong Prov Key Lab Comp Networks, Jinan 250000, Peoples R China 3.Shandong Prov Key Lab Novel Distributed Comp Softw, Jinan 250000, Peoples R China 4.Hebei Univ Econ & Business, Inst Informat Technol, Shijiazhuang 050061, Peoples R China 5.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China 6.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Hu, Chun-Yu,Hu, Li-Sha,Yuan, Lin,et al. FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2023,38(5):970-984.
APA	Hu, Chun-Yu,Hu, Li-Sha,Yuan, Lin,Lu, Dian-Jie,Lyu, Lei,&Chen, Yi-Qiang.(2023).FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,38(5),970-984.
MLA	Hu, Chun-Yu,et al."FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38.5(2023):970-984.