A Stochastic Gradient Approach for Communication Efficient Confederated Learning-Reference-Cited by-同舟云学术

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A Stochastic Gradient Approach for Communication Efficient Confederated Learning

Published:2024-04-14 Issue: Volume:22 Page:5170-5174
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Wang Bin¹,Fang Jun¹,Li Hongbin²,Eldar Yonina C.³

Affiliation:

1. University of Electronic Science and Technology of China (UESTC),Yangtze Delta Region Institute (Huzhou),Huzhou,China,313001

2. Stevens Institute of Technology,Department of Electrical and Computer Engineering,USA

3. Weizmann Institute of Science,Faculty of Mathematics and Computer Science,Israel

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10448035.pdf?arnumber=10448035

Reference23 articles.

1. Federated learning: Strategies for improving communication efficiency;Konečný,2016

2. Local SGD converges fast and communicates little;Stich

3. Federated accelerated stochastic gradient descent;Yuan;Advances in Neural Information Processing Systems,2020

4. Proxskip: Yes! local gradient steps provably lead to communication acceleration! finally!;Mishchenko

5. Cooperative SGD: A unified framework for the design and analysis of communication-efficient SGD algorithms;Wang;Journal of Machine Learning Research,2021

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