Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression

24 October 2022

Papers citing "Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression"

7 / 7 papers shown

Title
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Laurent Condat A. Maranjyan Peter Richtárik 39 3 0 07 Mar 2024
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities Aleksandr Beznosikov Martin Takáč Alexander Gasnikov 21 10 0 15 Feb 2023
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback Ilyas Fatkhullin Igor Sokolov Eduard A. Gorbunov Zhize Li Peter Richtárik 44 44 0 07 Oct 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 173 411 0 14 Jul 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 55 157 0 14 Feb 2021
Linearly Converging Error Compensated SGD Eduard A. Gorbunov D. Kovalev Dmitry Makarenko Peter Richtárik 163 77 0 23 Oct 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 159 760 0 28 Sep 2019