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FedComLoc: Communication-Efficient Distributed Training of Sparse and
  Quantized Models

FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models

14 March 2024
Kai Yi
Georg Meinhardt
Laurent Condat
Peter Richtárik
    FedML
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Papers citing "FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models"

6 / 6 papers shown
Title
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
Zhe Li
Bicheng Ying
Zidong Liu
Haibo Yang
Haibo Yang
FedML
59
3
0
24 May 2024
FedP3: Federated Personalized and Privacy-friendly Network Pruning under
  Model Heterogeneity
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
Kai Yi
Nidham Gazagnadou
Peter Richtárik
Lingjuan Lyu
79
11
0
15 Apr 2024
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
Laurent Condat
A. Maranjyan
Peter Richtárik
47
3
0
07 Mar 2024
GradSkip: Communication-Accelerated Local Gradient Methods with Better
  Computational Complexity
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
A. Maranjyan
M. Safaryan
Peter Richtárik
34
13
0
28 Oct 2022
A Field Guide to Federated Optimization
A Field Guide to Federated Optimization
Jianyu Wang
Zachary B. Charles
Zheng Xu
Gauri Joshi
H. B. McMahan
...
Mi Zhang
Tong Zhang
Chunxiang Zheng
Chen Zhu
Wennan Zhu
FedML
187
412
0
14 Jul 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity
  and Sparse Gradients
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
A. Mitra
Rayana H. Jaafar
George J. Pappas
Hamed Hassani
FedML
55
157
0
14 Feb 2021
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