TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation

20 February 2023

Papers citing "TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation"

9 / 9 papers shown

Title
Enhancing Privacy in Federated Learning through Local Training Nicola Bastianello Changxin Liu Karl H. Johansson 38 2 0 26 Mar 2024
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models Kai Yi Georg Meinhardt Laurent Condat Peter Richtárik FedML 32 6 0 14 Mar 2024
Correlated Quantization for Faster Nonconvex Distributed Optimization Andrei Panferov Yury Demidovich Ahmad Rammal Peter Richtárik MQ 23 4 0 10 Jan 2024
Revisiting Decentralized ProxSkip: Achieving Linear Speedup Luyao Guo Sulaiman A. Alghunaim Kun Yuan Laurent Condat Jinde Cao FedML 24 1 0 12 Oct 2023
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback Ilyas Fatkhullin Igor Sokolov Eduard A. Gorbunov Zhize Li Peter Richtárik 42 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 173 410 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 157 758 0 28 Sep 2019