Federated Random Reshuffling with Compression and Variance Reduction

8 May 2022

Papers citing "Federated Random Reshuffling with Compression and Variance Reduction"

10 / 10 papers shown

Title
Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation Grigory Malinovsky Umberto Michieli Hasan Hammoud Taha Ceritli Hayder Elesedy Mete Ozay Peter Richtárik AI4CE 20 1 0 10 Oct 2024
On the Last-Iterate Convergence of Shuffling Gradient Methods Zijian Liu Zhengyuan Zhou 21 2 0 12 Mar 2024
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction Yury Demidovich Grigory Malinovsky Peter Richtárik 50 2 0 11 Mar 2024
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Laurent Condat A. Maranjyan Peter Richtárik 34 3 0 07 Mar 2024
Towards a Better Theoretical Understanding of Independent Subnetwork Training Egor Shulgin Peter Richtárik AI4CE 16 6 0 28 Jun 2023
Improving Accelerated Federated Learning with Compression and Importance Sampling Michal Grudzieñ Grigory Malinovsky Peter Richtárik FedML 19 8 0 05 Jun 2023
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation Laurent Condat Ivan Agarský Grigory Malinovsky Peter Richtárik FedML 11 4 0 20 Feb 2023
Federated Learning with Regularized Client Participation Grigory Malinovsky Samuel Horváth Konstantin Burlachenko Peter Richtárik FedML 10 13 0 07 Feb 2023
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression Abdurakhmon Sadiev Grigory Malinovsky Eduard A. Gorbunov Igor Sokolov Ahmed Khaled Konstantin Burlachenko Peter Richtárik FedML 11 21 0 14 Jun 2022
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate Aryan Mokhtari Mert Gurbuzbalaban Alejandro Ribeiro 22 36 0 01 Nov 2016