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FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to
  System Heterogeneity
v1v2 (latest)

FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity

7 April 2022
Yonghai Gong
Yichuan Li
N. Freris
    FedML
ArXiv (abs)PDFHTML

Papers citing "FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity"

19 / 19 papers shown
Title
Controlling Participation in Federated Learning with Feedback
Controlling Participation in Federated Learning with Feedback
Michael Cummins
Güner Dilsad Er
Michael Muehlebach
FedML
134
0
0
28 Nov 2024
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
Yan Sun
Li Shen
Dacheng Tao
FedML
78
0
0
27 Sep 2024
Distributed Event-Based Learning via ADMM
Distributed Event-Based Learning via ADMM
Güner Dilsad Er
Sebastian Trimpe
Michael Muehlebach
FedML
93
2
0
17 May 2024
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity
  to Non-IID Data
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
65
233
0
22 May 2020
Federated Learning with Bayesian Differential Privacy
Federated Learning with Bayesian Differential Privacy
Aleksei Triastcyn
Boi Faltings
FedML
73
178
0
22 Nov 2019
Federated Learning with Differential Privacy: Algorithms and Performance
  Analysis
Federated Learning with Differential Privacy: Algorithms and Performance Analysis
Kang Wei
Jun Li
Ming Ding
Chuan Ma
Heng Yang
Farokhi Farhad
Shi Jin
Tony Q.S. Quek
H. Vincent Poor
FedML
127
1,624
0
01 Nov 2019
On the Convergence of Local Descent Methods in Federated Learning
On the Convergence of Local Descent Methods in Federated Learning
Farzin Haddadpour
M. Mahdavi
FedML
81
274
0
31 Oct 2019
Tighter Theory for Local SGD on Identical and Heterogeneous Data
Tighter Theory for Local SGD on Identical and Heterogeneous Data
Ahmed Khaled
Konstantin Mishchenko
Peter Richtárik
74
435
0
10 Sep 2019
On the Convergence of FedAvg on Non-IID Data
On the Convergence of FedAvg on Non-IID Data
Xiang Li
Kaixuan Huang
Wenhao Yang
Shusen Wang
Zhihua Zhang
FedML
149
2,348
0
04 Jul 2019
Towards Federated Learning at Scale: System Design
Towards Federated Learning at Scale: System Design
Keith Bonawitz
Hubert Eichner
W. Grieskamp
Dzmitry Huba
A. Ingerman
...
H. B. McMahan
Timon Van Overveldt
David Petrou
Daniel Ramage
Jason Roselander
FedML
124
2,670
0
04 Feb 2019
Federated Optimization in Heterogeneous Networks
Federated Optimization in Heterogeneous Networks
Tian Li
Anit Kumar Sahu
Manzil Zaheer
Maziar Sanjabi
Ameet Talwalkar
Virginia Smith
FedML
180
5,220
0
14 Dec 2018
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and
  Communication by Federated Learning
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
Xiaofei Wang
Yiwen Han
Chenyang Wang
Qiyang Zhao
Xu Chen
Min Chen
46
804
0
19 Sep 2018
Local SGD Converges Fast and Communicates Little
Local SGD Converges Fast and Communicates Little
Sebastian U. Stich
FedML
186
1,069
0
24 May 2018
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
  Algorithms
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao
Kashif Rasul
Roland Vollgraf
285
8,920
0
25 Aug 2017
CoCoA: A General Framework for Communication-Efficient Distributed
  Optimization
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
Virginia Smith
Simone Forte
Chenxin Ma
Martin Takáč
Michael I. Jordan
Martin Jaggi
79
273
0
07 Nov 2016
Federated Optimization: Distributed Machine Learning for On-Device
  Intelligence
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Jakub Konecný
H. B. McMahan
Daniel Ramage
Peter Richtárik
FedML
143
1,902
0
08 Oct 2016
Training Neural Networks Without Gradients: A Scalable ADMM Approach
Training Neural Networks Without Gradients: A Scalable ADMM Approach
Gavin Taylor
R. Burmeister
Zheng Xu
Bharat Singh
Ankit B. Patel
Tom Goldstein
ODL
69
276
0
06 May 2016
Communication-Efficient Learning of Deep Networks from Decentralized
  Data
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. B. McMahan
Eider Moore
Daniel Ramage
S. Hampson
Blaise Agüera y Arcas
FedML
406
17,559
0
17 Feb 2016
Asynchronous Distributed ADMM for Large-Scale Optimization- Part I:
  Algorithm and Convergence Analysis
Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis
Tsung-Hui Chang
Mingyi Hong
Wei-Cheng Liao
Xiangfeng Wang
43
200
0
09 Sep 2015
1