A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization

12 June 2020

Papers citing "A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization"

10 / 10 papers shown

Title
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$ -Means Clustering Lingxiao Huang Zhize Li Jialin Sun Haoyu Zhao FedML 31 9 0 26 Oct 2022
A simplified convergence theory for Byzantine resilient stochastic gradient descent Lindon Roberts E. Smyth 23 3 0 25 Aug 2022
Stochastic Gradient Methods with Preconditioned Updates Abdurakhmon Sadiev Aleksandr Beznosikov Abdulla Jasem Almansoori Dmitry Kamzolov R. Tappenden Martin Takáč ODL 21 9 0 01 Jun 2022
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression Haoyu Zhao Boyue Li Zhize Li Peter Richtárik Yuejie Chi 19 48 0 31 Jan 2022
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method Zhize Li 30 14 0 21 Mar 2021
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices Max Ryabinin Eduard A. Gorbunov Vsevolod Plokhotnyuk Gennady Pekhimenko 27 31 0 04 Mar 2021
MARINA: Faster Non-Convex Distributed Learning with Compression Eduard A. Gorbunov Konstantin Burlachenko Zhize Li Peter Richtárik 28 108 0 15 Feb 2021
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization Zhize Li Hongyan Bao Xiangliang Zhang Peter Richtárik ODL 24 125 0 25 Aug 2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Zhize Li D. Kovalev Xun Qian Peter Richtárik FedML AI4CE 18 133 0 26 Feb 2020
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 133 1,198 0 16 Aug 2016