EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback

9 June 2021

Papers citing "EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback"

36 / 36 papers shown

Title
$γ$ -FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning Rongwei Lu Yutong Jiang Jinrui Zhang Chunyang Li Yifei Zhu Bin Chen Zhi Wang FedML 7 0 0 18 May 2025
Convergence Analysis of Asynchronous Federated Learning with Gradient Compression for Non-Convex Optimization Diying Yang Yingwei Hou Danyang Xiao Weigang Wu FedML 39 0 0 28 Apr 2025
Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation Yaxiong Chen Yujie Wang Zixuan Zheng Jingliang Hu Yilei Shi Shengwu Xiong Xiao Xiang Zhu Lichao Mou 54 0 0 18 Mar 2025
Accelerated Distributed Optimization with Compression and Error Feedback Yuan Gao Anton Rodomanov Jeremy Rack Sebastian U. Stich 51 0 0 11 Mar 2025
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis Zhijie Chen Qiaobo Li A. Banerjee FedML 37 0 0 11 Nov 2024
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy Wei Huo Changxin Liu Kemi Ding Karl H. Johansson Ling Shi FedML 40 0 0 08 Aug 2024
Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point Bokun Wang Axel Berg D. A. E. Acar Chuteng Zhou FedML MQ 41 0 0 02 Jul 2024
Communication-efficient Vertical Federated Learning via Compressed Error Feedback Pedro Valdeira João Xavier Cláudia Soares Yuejie Chi FedML 45 4 0 20 Jun 2024
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Laurent Condat A. Maranjyan Peter Richtárik 47 4 0 07 Mar 2024
Correlated Quantization for Faster Nonconvex Distributed Optimization Andrei Panferov Yury Demidovich Ahmad Rammal Peter Richtárik MQ 44 4 0 10 Jan 2024
Sparse Training for Federated Learning with Regularized Error Correction Ran Greidi Kobi Cohen FedML 30 1 0 21 Dec 2023
Kimad: Adaptive Gradient Compression with Bandwidth Awareness Jihao Xin Ivan Ilin Shunkang Zhang Marco Canini Peter Richtárik 37 3 0 13 Dec 2023
Federated Learning is Better with Non-Homomorphic Encryption Konstantin Burlachenko Abdulmajeed Alrowithi Fahad Ali Albalawi Peter Richtárik FedML 44 6 0 04 Dec 2023
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
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation Tomàs Ortega Hamid Jafarkhani FedML 33 6 0 01 Aug 2023
Clip21: Error Feedback for Gradient Clipping Sarit Khirirat Eduard A. Gorbunov Samuel Horváth Rustem Islamov Fakhri Karray Peter Richtárik 32 10 0 30 May 2023
Error Feedback Shines when Features are Rare Peter Richtárik Elnur Gasanov Konstantin Burlachenko 36 2 0 24 May 2023
Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression Boyue Li Yuejie Chi 21 12 0 17 May 2023
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression Yutong He Xinmeng Huang Yiming Chen W. Yin Kun Yuan 31 7 0 12 May 2023
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features Aleksandr Beznosikov David Dobre Gauthier Gidel 25 5 0 23 Apr 2023
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression Avetik G. Karagulyan Peter Richtárik FedML 34 6 0 08 Mar 2023
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities Aleksandr Beznosikov Martin Takáč Alexander Gasnikov 29 10 0 15 Feb 2023
DoCoFL: Downlink Compression for Cross-Device Federated Learning Ron Dorfman S. Vargaftik Y. Ben-Itzhak Kfir Y. Levy FedML 32 19 0 01 Feb 2023
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence Kun-Yen Huang Shin-Yi Pu 35 9 0 14 Jan 2023
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression Xiaoyun Li Ping Li FedML 34 4 0 25 Nov 2022
Adaptive Compression for Communication-Efficient Distributed Training Maksim Makarenko Elnur Gasanov Rustem Islamov Abdurakhmon Sadiev Peter Richtárik 39 13 0 31 Oct 2022
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$ -Means Clustering Lingxiao Huang Zhize Li Jialin Sun Haoyu Zhao FedML 44 9 0 26 Oct 2022
On the Impossible Safety of Large AI Models El-Mahdi El-Mhamdi Sadegh Farhadkhani R. Guerraoui Nirupam Gupta L. Hoang Rafael Pinot Sébastien Rouault John Stephan 30 31 0 30 Sep 2022
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise D. A. Parletta Andrea Paudice Massimiliano Pontil Saverio Salzo 41 9 0 17 Aug 2022
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods Aleksandr Beznosikov Eduard A. Gorbunov Hugo Berard Nicolas Loizou 19 49 0 15 Feb 2022
FL_PyTorch: optimization research simulator for federated learning Konstantin Burlachenko Samuel Horváth Peter Richtárik FedML 48 18 0 07 Feb 2022
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression Haoyu Zhao Boyue Li Zhize Li Peter Richtárik Yuejie Chi 29 48 0 31 Jan 2022
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning S. Vargaftik Ran Ben-Basat Amit Portnoy Gal Mendelson Y. Ben-Itzhak Michael Mitzenmacher FedML 46 46 0 19 Aug 2021
Linearly Converging Error Compensated SGD Eduard A. Gorbunov D. Kovalev Dmitry Makarenko Peter Richtárik 163 78 0 23 Oct 2020
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization Zhize Li Hongyan Bao Xiangliang Zhang Peter Richtárik ODL 31 126 0 25 Aug 2020
Detached Error Feedback for Distributed SGD with Random Sparsification An Xu Heng-Chiao Huang 39 9 0 11 Apr 2020