CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression

20 July 2021

Papers citing "CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression"

8 / 8 papers shown

Title
Accelerated Distributed Optimization with Compression and Error Feedback Yuan Gao Anton Rodomanov Jeremy Rack Sebastian U. Stich 49 0 0 11 Mar 2025
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy Wei Huo Changxin Liu Kemi Ding Karl H. Johansson Ling Shi FedML 37 0 0 08 Aug 2024
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates Ahmad Rammal Kaja Gruntkowska Nikita Fedin Eduard A. Gorbunov Peter Richtárik 45 5 0 15 Oct 2023
Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression Boyue Li Yuejie Chi 21 12 0 17 May 2023
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$ -Means Clustering Lingxiao Huang Zhize Li Jialin Sun Haoyu Zhao FedML 41 9 0 26 Oct 2022
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression Haoyu Zhao Boyue Li Zhize Li Peter Richtárik Yuejie Chi 24 48 0 31 Jan 2022
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback Ilyas Fatkhullin Igor Sokolov Eduard A. Gorbunov Zhize Li Peter Richtárik 46 44 0 07 Oct 2021
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method Zhize Li 43 14 0 21 Mar 2021