Shifted Compression Framework: Generalizations and Improvements

21 June 2022

Papers citing "Shifted Compression Framework: Generalizations and Improvements"

18 / 18 papers shown

Title
MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization Laurent Condat Peter Richtárik 49 19 0 06 Jun 2021
FedNL: Making Newton-Type Methods Applicable to Federated Learning M. Safaryan Rustem Islamov Xun Qian Peter Richtárik FedML 69 80 0 05 Jun 2021
Linearly Converging Error Compensated SGD Eduard A. Gorbunov D. Kovalev Dmitry Makarenko Peter Richtárik 205 79 0 23 Oct 2020
PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning Thijs Vogels Sai Praneeth Karimireddy Martin Jaggi FedML 50 54 0 04 Aug 2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Zhize Li D. Kovalev Xun Qian Peter Richtárik FedML AI4CE 102 137 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 65 61 0 20 Feb 2020
Distributed Fixed Point Methods with Compressed Iterates Sélim Chraibi Ahmed Khaled D. Kovalev Peter Richtárik Adil Salim Martin Takávc FedML 47 17 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 256 6,261 0 10 Dec 2019
Gradient Descent with Compressed Iterates Ahmed Khaled Peter Richtárik 28 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 60 405 0 06 Jun 2019
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent Eduard A. Gorbunov Filip Hanzely Peter Richtárik 98 146 0 27 May 2019
Natural Compression for Distributed Deep Learning Samuel Horváth Chen-Yu Ho L. Horvath Atal Narayan Sahu Marco Canini Peter Richtárik 65 152 0 27 May 2019
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop D. Kovalev Samuel Horváth Peter Richtárik 98 156 0 24 Jan 2019
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training Liang Luo Jacob Nelson Luis Ceze Amar Phanishayee Arvind Krishnamurthy 106 120 0 21 May 2018
Gradient Sparsification for Communication-Efficient Distributed Optimization Jianqiao Wangni Jialei Wang Ji Liu Tong Zhang 87 526 0 26 Oct 2017
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour Priya Goyal Piotr Dollár Ross B. Girshick P. Noordhuis Lukasz Wesolowski Aapo Kyrola Andrew Tulloch Yangqing Jia Kaiming He 3DH 126 3,681 0 08 Jun 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 140 989 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 306 4,646 0 18 Oct 2016