AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation

27 April 2022

Papers citing "AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation"

31 / 31 papers shown

Title
The Key of Parameter Skew in Federated Learning Sifan Wang Junfeng Liao Ye Yuan Riquan Zhang FedML 53 0 0 21 Aug 2024
Federated Learning Based on Dynamic Regularization D. A. E. Acar Yue Zhao Ramon Matas Navarro Matthew Mattina P. Whatmough Venkatesh Saligrama FedML 37 762 0 08 Nov 2021
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models Liam H. Fowl Jonas Geiping W. Czaja Micah Goldblum Tom Goldstein FedML 103 146 0 25 Oct 2021
Data-Free Knowledge Distillation for Heterogeneous Federated Learning Zhuangdi Zhu Junyuan Hong Jiayu Zhou FedML 38 645 0 20 May 2021
A Variance Controlled Stochastic Method with Biased Estimation for Faster Non-convex Optimization Jia Bi S. Gunn 16 2 0 19 Feb 2021
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning Tomoya Murata Taiji Suzuki FedML 30 50 0 05 Feb 2021
On the Convergence of SGD with Biased Gradients Ahmad Ajalloeian Sebastian U. Stich 29 85 0 31 Jul 2020
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang Qinghua Liu Hao Liang Gauri Joshi H. Vincent Poor MoMe FedML 35 1,314 0 15 Jul 2020
Ensemble Distillation for Robust Model Fusion in Federated Learning Tao R. Lin Lingjing Kong Sebastian U. Stich Martin Jaggi FedML 45 1,026 0 12 Jun 2020
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data Xinwei Zhang Mingyi Hong S. Dhople W. Yin Yang Liu FedML 37 229 0 22 May 2020
FedSplit: An algorithmic framework for fast federated optimization Reese Pathak Martin J. Wainwright FedML 122 183 0 11 May 2020
FedDANE: A Federated Newton-Type Method Tian Li Anit Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar Virginia Smith FedML 94 156 0 07 Jan 2020
Variance Reduced Local SGD with Lower Communication Complexity Xian-Feng Liang Shuheng Shen Jingchang Liu Zhen Pan Enhong Chen Yifei Cheng FedML 56 152 0 30 Dec 2019
FedMD: Heterogenous Federated Learning via Model Distillation Daliang Li Junpu Wang FedML 67 845 0 08 Oct 2019
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum Jianyu Wang Vinayak Tantia Nicolas Ballas Michael G. Rabbat 39 201 0 01 Oct 2019
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification T. Hsu Qi Matthew Brown FedML 99 1,128 0 13 Sep 2019
Deep Leakage from Gradients Ligeng Zhu Zhijian Liu Song Han FedML 52 2,185 0 21 Jun 2019
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization Hao Yu Rong Jin Sen Yang FedML 56 381 0 09 May 2019
Federated Optimization in Heterogeneous Networks Tian Li Anit Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar Virginia Smith FedML 76 5,105 0 14 Dec 2018
Federated Learning with Non-IID Data Yue Zhao Meng Li Liangzhen Lai Naveen Suda Damon Civin Vikas Chandra FedML 132 2,547 0 02 Jun 2018
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 148 1,056 0 24 May 2018
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient Lam M. Nguyen Jie Liu K. Scheinberg Martin Takáč ODL 126 601 0 01 Mar 2017
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 89 1,886 0 08 Oct 2016
AIDE: Fast and Communication Efficient Distributed Optimization Sashank J. Reddi Jakub Konecný Peter Richtárik Barnabás Póczós Alex Smola 33 151 0 24 Aug 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 196 17,235 0 17 Feb 2016
Stop Wasting My Gradients: Practical SVRG Reza Babanezhad Mohamed Osama Ahmed Alim Virani Mark Schmidt Jakub Konecný Scott Sallinen 41 134 0 05 Nov 2015
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting Jakub Konecný Jie Liu Peter Richtárik Martin Takáč ODL 85 273 0 16 Apr 2015
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting Jakub Konecný Jie Liu Peter Richtárik Martin Takáč ODL 49 22 0 17 Oct 2014
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 135 738 0 19 Mar 2014
Communication Efficient Distributed Optimization using an Approximate Newton-type Method Ohad Shamir Nathan Srebro Tong Zhang 48 554 0 30 Dec 2013
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang 78 1,031 0 10 Sep 2012