Communication-Efficient Local SGD for Over-Parametrized Models with Partial Participation

Author:

Qin Tiancheng1,Yevale Jayesh1,Etesami S. Rasoul1

Affiliation:

1. University of Illinois Urbana-Champaign,Coordinated Science Lab,Department of Industrial and Systems Engineering,Urbana,IL,USA,61801

Funder

Air Force Office of Scientific Research

Publisher

IEEE

Reference25 articles.

1. Zipml: Training linear models with end-to-end low precision, and a little bit of deep learning;Zhang,2017

2. Deep gradient compression: Reducing the communication bandwidth for distributed training;Lin;arXiv preprint,2017

3. Optimal distributed online prediction using mini-batches;Dekel;Journal of Machine Learning Research,2012

4. Better mini-batch algorithms via accelerated gradient methods;Cotter;Advances in Neural Information Processing Systems,2011

5. Local SGD converges fast and communicates little;Stich;arXiv preprint,2018

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