Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification
in the Presence of Data Heterogeneity

Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity

19 February 2023

Papers citing "Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity"

10 / 10 papers shown

Title
FetchSGD: Communication-Efficient Federated Learning with Sketching D. Rothchild Ashwinee Panda Enayat Ullah Nikita Ivkin Ion Stoica Vladimir Braverman Joseph E. Gonzalez Raman Arora FedML 48 363 0 15 Jul 2020
Federated Learning with Compression: Unified Analysis and Sharp Guarantees Farzin Haddadpour Mohammad Mahdi Kamani Aryan Mokhtari M. Mahdavi FedML 61 275 0 02 Jul 2020
UVeQFed: Universal Vector Quantization for Federated Learning Nir Shlezinger Mingzhe Chen Yonina C. Eldar H. Vincent Poor Shuguang Cui FedML MQ 44 225 0 05 Jun 2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Zhize Li D. Kovalev Xun Qian Peter Richtárik FedML AI4CE 81 136 0 26 Feb 2020
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification T. Hsu Qi Matthew Brown FedML 111 1,128 0 13 Sep 2019
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression Hanlin Tang Xiangru Lian Chen Yu Tong Zhang Ji Liu 33 217 0 15 May 2019
Robust and Communication-Efficient Federated Learning from Non-IID Data Felix Sattler Simon Wiedemann K. Müller Wojciech Samek FedML 44 1,343 0 07 Mar 2019
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 154 1,056 0 24 May 2018
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Chengyue Wu Song Han Huizi Mao Yu Wang W. Dally 107 1,399 0 05 Dec 2017
Revisiting Distributed Synchronous SGD Jianmin Chen Xinghao Pan R. Monga Samy Bengio Rafal Jozefowicz 66 799 0 04 Apr 2016