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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

文献类型：期刊论文

作者Hu, Chun-Yu4,5,6; Hu, Li-Sha3; Yuan, Lin5,6; Lu, Dian-Jie2,4; Lyu, Lei2,4; Chen, Yi-Qiang1
刊名JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
出版日期2023-09-01
卷号38期号:5页码:970-984
关键词federated learning incremental learning random forest wearable health monitoring
ISSN号1000-9000
DOI10.1007/s11390-023-3009-0
英文摘要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.
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
语种英语
WOS记录号WOS:001114345700009
出版者SPRINGER SINGAPORE PTE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/38455]  
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.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
2.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China
3.Hebei Univ Econ & Business, Inst Informat Technol, Shijiazhuang 050061, Peoples R China
4.Shandong Prov Key Lab Novel Distributed Comp Softw, Jinan 250000, Peoples R China
5.Shandong Fundamental Res Ctr Comp Sci, Shandong Prov Key Lab Comp Networks, Jinan 250000, Peoples R China
6.Qilu Univ Technol, Shandong Acad Sci, Shandong Comp Sci Ctr, Natl Supercomp Ctr Jinan,Key Lab Comp Power Networ, Jinan 250353, 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.

入库方式： OAI收割

来源：计算技术研究所

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