MetaFed: Federated Learning Among Federations With Cyclic Knowledge Distillation for Personalized Healthcare

doi:10.1109/TNNLS.2023.3297103

	MetaFed: Federated Learning Among Federations With Cyclic Knowledge Distillation for Personalized Healthcare
	Chen, Yiqiang 1; Lu, Wang 1; Qin, Xin 1; Wang, Jindong 2; Xie, Xing 2
	2023-07-28
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
页码	12
摘要	Federated learning (FL) has attracted increasing attention to building models without accessing raw user data, especially in healthcare. In real applications, different federations can seldom work together due to possible reasons such as data heterogeneity and distrust/inexistence of the central server. In this article, we propose a novel framework called MetaFed to facilitate trustworthy FL between different federations. obtains a personalized model for each federation without a central server via the proposed cyclic knowledge distillation. Specifically, treats each federation as a meta distribution and aggregates knowledge of each federation in a cyclic manner. The training is split into two parts: common knowledge accumulation and personalization. Comprehensive experiments on seven benchmarks demonstrate that without a server achieves better accuracy compared with state-of-the-art methods e.g., 10%+ accuracy improvement compared with the baseline for physical activity monitoring dataset (PAMAP2) with fewer communication costs. More importantly, shows remarkable performance in real-healthcare-related applications.
关键词	Federated learning (FL) healthcare knowledge distillation (KD) personalization transfer learning
DOI	10.1109/TNNLS.2023.3297103
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan of China[2021YFC2501202] ; Natural Science Foundation of China[61972383] ; Natural Science Foundation of China[62202455] ; Beijing Municipal Science and Technology Commission[Z221100002722009] ; Science Research Foundation of the Joint Laboratory Project on Digital Ophthalmology and Vision Science[SZYK202201]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:001043272300001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：24[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/2XEOYT63/21328
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, Yiqiang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Microsoft Res Asia, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Chen, Yiqiang,Lu, Wang,Qin, Xin,et al. MetaFed: Federated Learning Among Federations With Cyclic Knowledge Distillation for Personalized Healthcare[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:12.
APA	Chen, Yiqiang,Lu, Wang,Qin, Xin,Wang, Jindong,&Xie, Xing.(2023).MetaFed: Federated Learning Among Federations With Cyclic Knowledge Distillation for Personalized Healthcare.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12.
MLA	Chen, Yiqiang,et al."MetaFed: Federated Learning Among Federations With Cyclic Knowledge Distillation for Personalized Healthcare".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):12.