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GADMM: fast and communication efficient framework for distributed machine learning

Elgabli, Anis, Park, Jihong, Bedi, Amrit S., Bennis, Mehdi and Aggarwal, Vaneet 2020, GADMM: fast and communication efficient framework for distributed machine learning, Journal of machine learning research, vol. 21, pp. 1-39.

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Title GADMM: fast and communication efficient framework for distributed machine learning
Author(s) Elgabli, Anis
Park, JihongORCID iD for Park, Jihong orcid.org/0000-0001-7623-6552
Bedi, Amrit S.
Bennis, Mehdi
Aggarwal, Vaneet
Journal name Journal of machine learning research
Volume number 21
Article ID 76
Start page 1
End page 39
Total pages 39
Publisher Journal of Machine Learning Research
Place of publication [United States]
Publication date 2020
ISSN 1533-7928
Keyword(s) Science & Technology
Technology
Automation & Control Systems
Computer Science, Artificial Intelligence
Computer Science
OPTIMIZATION
CONSENSUS
CONVERGENCE
ALGORITHM
ADMM
Language eng
Indigenous content off
Field of Research 08 Information and Computing Sciences
17 Psychology and Cognitive Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30139695

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.