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Distributed Learning with Compressed Gradient Differences

26 January 2019

Konstantin Mishchenko

Papers citing "Distributed Learning with Compressed Gradient Differences"

27 / 77 papers shown

Title
MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization Laurent Condat Peter Richtárik 75 19 0 06 Jun 2021
FedNL: Making Newton-Type Methods Applicable to Federated Learning M. Safaryan Rustem Islamov Xun Qian Peter Richtárik FedML 89 80 0 05 Jun 2021
Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates Laizhong Cui Xiaoxin Su Yipeng Zhou Yi Pan FedML 73 37 0 10 May 2021
BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated Learning He Zhu Qing Ling FedML AAML 146 13 0 14 Apr 2021
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices Max Ryabinin Eduard A. Gorbunov Vsevolod Plokhotnyuk Gennady Pekhimenko 131 35 0 04 Mar 2021
IntSGD: Adaptive Floatless Compression of Stochastic Gradients Konstantin Mishchenko Bokun Wang D. Kovalev Peter Richtárik 100 15 0 16 Feb 2021
MARINA: Faster Non-Convex Distributed Learning with Compression Eduard A. Gorbunov Konstantin Burlachenko Zhize Li Peter Richtárik 100 110 0 15 Feb 2021
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization M. Safaryan Filip Hanzely Peter Richtárik 42 24 0 14 Feb 2021
Distributed Second Order Methods with Fast Rates and Compressed Communication Rustem Islamov Xun Qian Peter Richtárik 86 51 0 14 Feb 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 112 160 0 14 Feb 2021
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity Amirhossein Reisizadeh Isidoros Tziotis Hamed Hassani Aryan Mokhtari Ramtin Pedarsani FedML 231 102 0 28 Dec 2020
Recent Theoretical Advances in Non-Convex Optimization Marina Danilova Pavel Dvurechensky Alexander Gasnikov Eduard A. Gorbunov Sergey Guminov Dmitry Kamzolov Innokentiy Shibaev 129 79 0 11 Dec 2020
Local SGD: Unified Theory and New Efficient Methods Eduard A. Gorbunov Filip Hanzely Peter Richtárik FedML 93 111 0 03 Nov 2020
Linearly Converging Error Compensated SGD Eduard A. Gorbunov D. Kovalev Dmitry Makarenko Peter Richtárik 214 78 0 23 Oct 2020
Optimal Gradient Compression for Distributed and Federated Learning Alyazeed Albasyoni M. Safaryan Laurent Condat Peter Richtárik FedML 67 64 0 07 Oct 2020
On Communication Compression for Distributed Optimization on Heterogeneous Data Sebastian U. Stich 93 23 0 04 Sep 2020
Linear Convergent Decentralized Optimization with Compression Xiaorui Liu Yao Li Rongrong Wang Jiliang Tang Ming Yan 65 46 0 01 Jul 2020
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees Constantin Philippenko Aymeric Dieuleveut FedML 89 51 0 25 Jun 2020
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning Samuel Horváth Peter Richtárik 79 60 0 19 Jun 2020
Federated Accelerated Stochastic Gradient Descent Honglin Yuan Tengyu Ma FedML 100 180 0 16 Jun 2020
Detached Error Feedback for Distributed SGD with Random Sparsification An Xu Heng-Chiao Huang 71 9 0 11 Apr 2020
On Biased Compression for Distributed Learning Aleksandr Beznosikov Samuel Horváth Peter Richtárik M. Safaryan 75 189 0 27 Feb 2020
Differentially Quantized Gradient Methods Chung-Yi Lin V. Kostina B. Hassibi MQ 66 8 0 06 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
Gradient Descent with Compressed Iterates Ahmed Khaled Peter Richtárik 41 22 0 10 Sep 2019
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent Eduard A. Gorbunov Filip Hanzely Peter Richtárik 111 147 0 27 May 2019
Natural Compression for Distributed Deep Learning Samuel Horváth Chen-Yu Ho L. Horvath Atal Narayan Sahu Marco Canini Peter Richtárik 91 152 0 27 May 2019