University of Oulu

A. Elgabli, J. Park, A. S. Bedi, M. Bennis and V. Aggarwal, "Communication Efficient Framework for Decentralized Machine Learning," 2020 54th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA, 2020, pp. 1-5, doi: 10.1109/CISS48834.2020.1570627384

Communication efficient framework for decentralized machine learning

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Author: Elgabli, Anis1; Park, Jihong1; Bedi, Amrit S.2;
Organizations: 1University of Oulu, Finland
2Army Research Lab, USA
3Purdue University, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020100277669
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-10-02
Description:

Abstract

In this paper, we propose a fast, privacy-aware, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem. The proposed algorithm is based on the Alternating Direction Method of Multipliers (ADMM) algorithm. The key novelty in the proposed algorithm is that it solves the problem in a decentralized topology where at most half of the workers are competing the limited communication resources at any given time. Moreover, each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with a lower amount of communication overhead in each exchange. We prove that GADMM converges faster than the centralized batch gradient descent for convex loss functions, and numerically show that it converges faster and more communication-efficient than the state-of-the-art communication-efficient algorithms such as the Lazily Aggregated Gradient (LAG) and dual averaging, in linear and logistic regression tasks on synthetic and real datasets.

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ISBN: 978-1-7281-4085-8
ISBN Print: 978-1-7281-8831-7
Pages: 1 - 5
DOI: 10.1109/CISS48834.2020.1570627384
OADOI: https://oadoi.org/10.1109/CISS48834.2020.1570627384
Host publication: 54th Annual Conference on Information Sciences and Systems, CISS 2020
Conference: Annual Conference on Information Sciences and Systems, CISS
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
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