# EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback

@inproceedings{Richtrik2021EF21AN, title={EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback}, author={Peter Richt{\'a}rik and Igor Sokolov and Ilyas Fatkhullin}, booktitle={NeurIPS}, year={2021} }

Error feedback (EF), also known as error compensation, is an immensely popular convergence stabilization mechanism in the context of distributed training of supervised machine learning models enhanced by the use of contractive communication compression mechanisms, such as Top-k. First proposed by Seide et al. [2014] as a heuristic, EF resisted any theoretical understanding until recently [Stich et al., 2018, Alistarh et al., 2018]. While these early breakthroughs were followed by a steady…

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