Model Sparsification for Communication-Efficient Multi-Party Learning via Contrastive Distillation in Image Classification-Reference-Cited by-同舟云学术

Model Sparsification for Communication-Efficient Multi-Party Learning via Contrastive Distillation in Image Classification

Published:2024-02 Issue:1 Volume:8 Page:150-163
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Feng Kai-Yuan¹^ORCID,Gong Maoguo²^ORCID,Pan Ke³,Zhao Hongyu²,Wu Yue⁴^ORCID,Sheng Kai¹

Affiliation:

1. Key Laboratory of Collaborative Intelligence Systems, Ministry of Education and the Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, China

2. Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, and the School of Electronic Engineering, Xidian University, Xi'an, China

3. Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, and the School of Cyber Engineering, Xidian University, Xi'an, China

4. Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, and the School of Computer Science and Technology, Xidian University, Xi'an, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/7433297/10412693/10115455.pdf?arnumber=10115455

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