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Synthetic data shuffling accelerates the convergence of federated
  learning under data heterogeneity
v1v2 (latest)

Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity

23 June 2023
Yue Liu
Yasin Esfandiari
Mikkel N. Schmidt
T. S. Alstrøm
Sebastian U. Stich
    FedML
ArXiv (abs)PDFHTML

Papers citing "Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity"

1 / 1 papers shown
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Sai Praneeth Karimireddy
Martin Jaggi
Satyen Kale
M. Mohri
Sashank J. Reddi
Sebastian U. Stich
A. Suresh
FedML
548
236
0
08 Aug 2020
1