Multiparty Dual Learning-Reference-Cited by-同舟云学术

Multiparty Dual Learning

Published:2023-05 Issue:5 Volume:53 Page:2955-2968
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Gao Yuan¹^ORCID,Gong Maoguo¹^ORCID,Xie Yu²^ORCID,Qin A. K.³^ORCID,Pan Ke¹,Ong Yew-Soon⁴^ORCID

Affiliation:

1. School of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, Shaanxi, China

2. Key Laboratory of Computational Intelligence and Chinese Information Processing, Ministry of Education, Shanxi University, Taiyuan, China

3. Department of Computing Technologies, Swinburne University of Technology, Hawthorn, VIC, Australia

4. Agency for Science, Technology and Research, and the School of Computer Science and Engineering, Nanyang Technological University, Singapore

Funder

National Natural Science Foundation of China

Australian Research Council

A*STAR Centre for Frontier AI Research (CFAR) and the Data Science Artificial Intelligence Research Center (DSAIR) at School of Computer Science and Engineering, Nanyang Technological University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/10106628/09686623.pdf?arnumber=9686623

Reference49 articles.

1. Privacy-Preserving Deep Learning

2. Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence

3. Federated adversarial domain adaptation;peng;Proc 8th Int Conf Learn Represent,2020

4. Privacy-Preserving Deep Learning via Additively Homomorphic Encryption

5. A Survey on Transfer Learning

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