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EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization

9 May 2022

Papers citing "EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization"

23 / 23 papers shown

Title
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Laurent Condat Artavazd Maranjyan Peter Richtárik 123 5 0 07 Mar 2024
EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback Ilyas Fatkhullin Igor Sokolov Eduard A. Gorbunov Zhize Li Peter Richtárik 115 47 0 07 Oct 2021
MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization Laurent Condat Peter Richtárik 52 19 0 06 Jun 2021
Innovation Compression for Communication-efficient Distributed Optimization with Linear Convergence Jiaqi Zhang Keyou You Lihua Xie 59 32 0 14 May 2021
Linearly Converging Error Compensated SGD Eduard A. Gorbunov D. Kovalev Dmitry Makarenko Peter Richtárik 209 78 0 23 Oct 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark Schmidt Francis R. Bach Peter Richtárik 88 117 0 02 Oct 2020
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees Constantin Philippenko Aymeric Dieuleveut FedML 85 51 0 25 Jun 2020
rTop-k: A Statistical Estimation Approach to Distributed SGD L. P. Barnes Huseyin A. Inan Berivan Isik Ayfer Özgür 65 65 0 21 May 2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Zhize Li D. Kovalev Xun Qian Peter Richtárik FedML AI4CE 116 137 0 26 Feb 2020
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor M. Safaryan Egor Shulgin Peter Richtárik 80 61 0 20 Feb 2020
A Survey on Distributed Machine Learning Joost Verbraeken Matthijs Wolting Jonathan Katzy Jeroen Kloppenburg Tim Verbelen Jan S. Rellermeyer OOD 105 713 0 20 Dec 2019
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 275 6,294 0 10 Dec 2019
Gradient Descent with Compressed Iterates Ahmed Khaled Peter Richtárik 31 22 0 10 Sep 2019
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations Debraj Basu Deepesh Data C. Karakuş Suhas Diggavi MQ 67 406 0 06 Jun 2019
One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods Filip Hanzely Peter Richtárik 88 27 0 27 May 2019
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent Eduard A. Gorbunov Filip Hanzely Peter Richtárik 105 147 0 27 May 2019
Natural Compression for Distributed Deep Learning Samuel Horváth Chen-Yu Ho L. Horvath Atal Narayan Sahu Marco Canini Peter Richtárik 83 152 0 27 May 2019
Robust and Communication-Efficient Federated Learning from Non-IID Data Felix Sattler Simon Wiedemann K. Müller Wojciech Samek FedML 79 1,362 0 07 Mar 2019
Gradient Sparsification for Communication-Efficient Distributed Optimization Jianqiao Wangni Jialei Wang Ji Liu Tong Zhang 100 528 0 26 Oct 2017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning W. Wen Cong Xu Feng Yan Chunpeng Wu Yandan Wang Yiran Chen Hai Helen Li 184 990 0 22 May 2017
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 312 4,657 0 18 Oct 2016
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark Schmidt 280 1,221 0 16 Aug 2016
Parallel Coordinate Descent Methods for Big Data Optimization Peter Richtárik Martin Takáč 130 487 0 04 Dec 2012