Federated unsupervised representation learning

To leverage the enormous amount of unlabeled data on distributed edge devices, we formulate a new problem in federated learning called federated unsupervised representation learning (FURL) to learn a common representation model without supervision while preserving data privacy. FURL poses two new ch...

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Bibliographic Details
Published in:	Frontiers of information technology & electronic engineering Vol. 24; no. 8; pp. 1181 - 1193
Main Authors:	Zhang, Fengda, Kuang, Kun, Chen, Long, You, Zhaoyang, Shen, Tao, Xiao, Jun, Zhang, Yin, Wu, Chao, Wu, Fei, Zhuang, Yueting, Li, Xiaolin
Format:	Journal Article
Language:	English
Published:	Hangzhou Zhejiang University Press 01-08-2023 Springer Nature B.V College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China%School of Public Affairs,Zhejiang University,Hangzhou 310027,China%Tongdun Technology,Hangzhou 310000,China ElasticMind.AI Technology Inc.,Hangzhou 310018,China Institute of Basic Medicine and Cancer,Chinese Academy of Sciences,Hangzhou 310018,China
Subjects:	Algorithms Alignment Clients Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Dictionaries Electrical Engineering Electronics and Microelectronics Instrumentation Modules Networks Representations Research Article Representation learning Federated learning 无监督学习 TP183 Contrastive learning 表示学习对比学习联邦学习 Unsupervised learning
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