Communication-Efficient Distributed Learning: An Overview

Author:

Cao Xuanyu1ORCID,Başar Tamer2ORCID,Diggavi Suhas3ORCID,Eldar Yonina C.4ORCID,Letaief Khaled B.1ORCID,Poor H. Vincent5ORCID,Zhang Junshan6ORCID

Affiliation:

1. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

2. Department of Electrical and Computer Engineering, University of Illinois Urbana–Champaign, Urbana–Champaign, IL, USA

3. Department of Electrical and Computer Engineering, The University of California, Los Angeles, CA, USA

4. Department of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel

5. Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA

6. Department of Electrical and Computer Engineering, The University of California, Davis, CA, USA

Funder

U.S. National Science Foundation

U.S. Army Research Office under MURI

U.S. Army Research Laboratory Cooperative

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Reference183 articles.

1. Cooperative SGD: A unified framework for the design and analysis of local-update SGD algorithms;wang;J Mach Learn Res,2021

2. Deep learning with elastic averaging SGD;zhang;Adv Neural Inf Process Syst,2015

3. Semi-Decentralized Federated Learning With Cooperative D2D Local Model Aggregations

4. Communication-efficient algorithms for decentralized and stochastic optimization

5. Tackling the objective inconsistency problem in heterogeneous federated optimization;wang;Adv Neural Inf Process Syst,2020

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