Preserved central model for faster bidirectional compression in
distributed settings

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Preserved central model for faster bidirectional compression in distributed settings

24 February 2021

Constantin Philippenko

Aymeric Dieuleveut

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Papers citing "Preserved central model for faster bidirectional compression in distributed settings"

15 / 15 papers shown

Title
Tight analyses of first-order methods with error feedback Daniel Berg Thomsen Adrien B. Taylor Aymeric Dieuleveut 96 1 0 05 Jun 2025
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Laurent Condat Artavazd Maranjyan Peter Richtárik 123 5 0 07 Mar 2024
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates Ahmad Rammal Kaja Gruntkowska Nikita Fedin Eduard A. Gorbunov Peter Richtárik 85 5 0 15 Oct 2023
Knowledge Distillation Performs Partial Variance Reduction M. Safaryan Alexandra Peste Dan Alistarh 84 7 0 27 May 2023
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression Yutong He Xinmeng Huang Yiming Chen W. Yin Kun Yuan 113 7 0 12 May 2023
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation Laurent Condat Ivan Agarský Grigory Malinovsky Peter Richtárik FedML 108 4 0 20 Feb 2023
DoCoFL: Downlink Compression for Cross-Device Federated Learning Ron Dorfman S. Vargaftik Y. Ben-Itzhak Kfir Y. Levy FedML 101 22 0 01 Feb 2023
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression Laurent Condat Ivan Agarský Peter Richtárik FedML 113 17 0 24 Oct 2022
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression Kaja Gruntkowska Alexander Tyurin Peter Richtárik 150 24 0 30 Sep 2022
Downlink Compression Improves TopK Sparsification William Zou H. Sterck Jun Liu 38 0 0 30 Sep 2022
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression Xinmeng Huang Yiming Chen W. Yin Kun Yuan 92 27 0 08 Jun 2022
FedShuffle: Recipes for Better Use of Local Work in Federated Learning Samuel Horváth Maziar Sanjabi Lin Xiao Peter Richtárik Michael G. Rabbat FedML 88 21 0 27 Apr 2022
Federated Expectation Maximization with heterogeneity mitigation and variance reduction Aymeric Dieuleveut G. Fort Eric Moulines Geneviève Robin FedML 86 5 0 03 Nov 2021
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees Constantin Philippenko Aymeric Dieuleveut FedML 93 51 0 25 Jun 2020
Distributed Learning with Compressed Gradient Differences Konstantin Mishchenko Eduard A. Gorbunov Martin Takáč Peter Richtárik 142 202 0 26 Jan 2019