Trading redundancy for communication: Speeding up Distributed SGD for Non-convex Optimization \

Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck R. Cadambe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Communication overhead is one of the key challenges that hinders the scalability of distributed optimization algorithms to train large neural networks. In recent years, there has been a great deal of research to alleviate communication cost by compressing the gradient vector or using local updates and periodic model averaging. In this paper, we advocate the use of redundancy towards communication-efficient distributed stochastic algorithms for non-convex optimization. In particular, we, both theoretically and practically, show that by properly infusing redundancy to the training data with model averaging, it is possible to significantly reduce the number of communication rounds. To be more precise, we show that redundancy reduces residual error in local averaging, thereby reaching the same level of accuracy with fewer rounds of communication as compared with previous algorithms. Empirical studies on CIFAR10, CIFAR100 and ImageNet datasets in a distributed environment complement our theoretical results; they show that our algorithms have additional beneficial aspects including tolerance to failures, as well as greater gradient diversity.

Original languageEnglish (US)
Title of host publication36th International Conference on Machine Learning, ICML 2019
PublisherInternational Machine Learning Society (IMLS)
Pages4497-4527
Number of pages31
ISBN (Electronic)9781510886988
StatePublished - Jan 1 2019
Event36th International Conference on Machine Learning, ICML 2019 - Long Beach, United States
Duration: Jun 9 2019Jun 15 2019

Publication series

Name36th International Conference on Machine Learning, ICML 2019
Volume2019-June

Conference

Conference36th International Conference on Machine Learning, ICML 2019
CountryUnited States
CityLong Beach
Period6/9/196/15/19

All Science Journal Classification (ASJC) codes

  • Education
  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Haddadpour, F., Kamani, M. M., Mahdavi, M., & Cadambe, V. R. (2019). Trading redundancy for communication: Speeding up Distributed SGD for Non-convex Optimization \. In 36th International Conference on Machine Learning, ICML 2019 (pp. 4497-4527). (36th International Conference on Machine Learning, ICML 2019; Vol. 2019-June). International Machine Learning Society (IMLS).