Fast Decentralized Learning Via Hybrid Consensus Admm

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Abstract

The Alternating Directions Methods of Multipliers (ADMM) has witnessed a resurgence of interest over the past few years fueled by the ever increasing demand for scalable optimization techniques to tackle real-world statistical learning problems. However, despite its success in several application settings the applicability of the traditional centralized ADMM is limited by its communication requirement to a global fusion center, which might not be always feasible. Its decentralized variant D-CADMM, on the other hand, while it alleviates this need, it does so at the expense of significantly slower convergence in cases of adverse underlying network topologies. To address the aforementioned limitations, in this work we consider the presence of multiple fusion centers and we propose a unifying framework that allows leveraging the structure of the communication network to accelerate the decentralized ADMM even in cases where it is not practical to resort to its fully centralized counterpart. We prove the linear convergence rate of the proposed approach and we verify its promising performance by carrying out numerical tests on both real and synthetic networks.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3829-3833
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: Apr 15 2018Apr 20 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period4/15/184/20/18

Bibliographical note

Funding Information:
The work in this paper was supported by NSF grants 1500713, 1508993, 1509040, 1711471.

Funding Information:
The work in this paper was supported by NSF grants 1500713, 1508993, 1509040, 1711471

Keywords

  • Distributed optimization
  • ADMM algorithm
  • Decentralized Learning
  • Consensus algorithm
  • multi-agent networks

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