Laplacian Matrix Sampling for Communication- Efficient Decentralized Learning-Reference-Cited by-同舟云学术

Laplacian Matrix Sampling for Communication- Efficient Decentralized Learning

Published:2023-04 Issue:4 Volume:41 Page:887-901
ISSN:0733-8716
Container-title:IEEE Journal on Selected Areas in Communications
language:
Short-container-title:IEEE J. Select. Areas Commun.

Author:

Chiu Cho-Chun¹^ORCID,Zhang Xusheng¹^ORCID,He Ting¹^ORCID,Wang Shiqiang²^ORCID,Swami Ananthram³

Affiliation:

1. Computer Science and Engineering, The Pennsylvania State University, University Park, PA, USA

2. IBM T. J. Watson Research Center, Yorktown Heights, NY, USA

3. DEVCOM Army Research Laboratory, Adelphi, MD, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/49/10075675/10038615.pdf?arnumber=10038615

Reference40 articles.

1. Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent;lian;Proc 31st Int Conf Neural Inf Process Syst,2017

2. Adaptive Federated Learning in Resource Constrained Edge Computing Systems

3. Link-level measurements from an 802.11b mesh network

4. D2: Decentralized training over decentralized data;tang;Proc 35th Int Conf Mach Learn,2018

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