Communication-Efficient Federated Learning: A Variance-Reduced Stochastic Approach With Adaptive Sparsification-Reference-Cited by-同舟云学术

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Communication-Efficient Federated Learning: A Variance-Reduced Stochastic Approach With Adaptive Sparsification

Published:2023 Issue: Volume:71 Page:3562-3576
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Wang Bin¹^ORCID,Fang Jun¹^ORCID,Li Hongbin²^ORCID,Zeng Bing³^ORCID

Affiliation:

1. National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu, China

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

3. School of Information and Communications Engineering, University of Electronic Science and Technology of China, Chengdu, China

Funder

National Natural Science Foundation of China

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/78/10040758/10256151.pdf?arnumber=10256151

Reference51 articles.

1. FedPD: A Federated Learning Framework With Adaptivity to Non-IID Data

2. Convergence of Distributed Stochastic Variance Reduced Methods Without Sampling Extra Data

3. Federated optimization in heterogeneous networks;li;Proc Mach Learn Syst,0

4. On the convergence of FedAvg on non-IID data;li,2019

5. Variance reduced local SGD with lower communication complexity;liang,2019

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