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On the Convergence of Federated Learning Algorithms without Data
  Similarity

On the Convergence of Federated Learning Algorithms without Data Similarity

29 February 2024
Ali Beikmohammadi
Sarit Khirirat
Sindri Magnússon
    FedML
ArXivPDFHTML

Papers citing "On the Convergence of Federated Learning Algorithms without Data Similarity"

5 / 5 papers shown
Title
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern
  Error Feedback
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback
Ilyas Fatkhullin
Igor Sokolov
Eduard A. Gorbunov
Zhize Li
Peter Richtárik
46
44
0
07 Oct 2021
On the Convergence of Step Decay Step-Size for Stochastic Optimization
On the Convergence of Step Decay Step-Size for Stochastic Optimization
Xiaoyu Wang
Sindri Magnússon
M. Johansson
66
23
0
18 Feb 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
Linearly Converging Error Compensated SGD
Linearly Converging Error Compensated SGD
Eduard A. Gorbunov
D. Kovalev
Dmitry Makarenko
Peter Richtárik
163
77
0
23 Oct 2020
FedPAQ: A Communication-Efficient Federated Learning Method with
  Periodic Averaging and Quantization
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Amirhossein Reisizadeh
Aryan Mokhtari
Hamed Hassani
Ali Jadbabaie
Ramtin Pedarsani
FedML
174
760
0
28 Sep 2019
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