MetaFed: Federated Learning Among Federations With Cyclic Knowledge Distillation for Personalized Healthcare
文献类型：期刊论文
| 作者 | Chen, Yiqiang2; Lu, Wang2; Qin, Xin2; Wang, Jindong1; Xie, Xing1 |
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2023-07-28 |
| 页码 | 12 |
| ISSN号 | 2162-237X |
| 关键词 | Federated learning (FL) healthcare knowledge distillation (KD) personalization transfer learning |
| DOI | 10.1109/TNNLS.2023.3297103 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001043272300001 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21327]  |
| 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.Microsoft Res Asia, Beijing 100080, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, 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. |
入库方式： OAI收割
来源：计算技术研究所
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