Communication Efficient Federated Learning With Heterogeneous Structured Client Models-Reference-Cited by-同舟云学术

Communication Efficient Federated Learning With Heterogeneous Structured Client Models

Published:2022 Issue: Volume: Page:1-15
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Hu Yao¹^ORCID,Sun Xiaoyan²^ORCID,Tian Ye³^ORCID,Song Linqi¹^ORCID,Tan Kay Chen⁴^ORCID

Affiliation:

1. Department of Computer Science, City University of Hong Kong, Hong Kong SAR

2. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China

3. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China

4. Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR

Funder

National Natural Science Foundation of China

Research Grants Council of the Hong Kong, SAR

Changsha Science and Technology Program International and Regional Science and Technology Cooperation Project

Technological Breakthrough Project of Science, Technology and Innovation Commission of Shenzhen Municipality

InnoHK Initiative

Government of the HKSAR

Laboratory for AI-Powered Financial Technologies

Hong Kong UGC Special Virtual Teaching and Learning

Hong Kong UGC RMGS

Tencent AI Lab Rhino-Bird Gift Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computational Mathematics,Control and Optimization,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7433297/7777658/09911782.pdf?arnumber=9911782

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