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Federated Multi-Mini-Batch: An Efficient Training Approach to Federated
  Learning in Non-IID Environments
v1v2 (latest)

Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments

13 November 2020
Reza Nasirigerdeh
Mohammad Bakhtiari
Reihaneh Torkzadehmahani
Amirhossein Bayat
M. List
David B. Blumenthal
Jan Baumbach
    FedML
ArXiv (abs)PDFHTML

Papers citing "Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments"

3 / 3 papers shown
Optimisation of federated learning settings under statistical
  heterogeneity variations
Optimisation of federated learning settings under statistical heterogeneity variations
Basem Suleiman
M. J. Alibasa
R. Purwanto
Lewis Jeffries
Ali Anaissi
Jacky Song
FedML
308
0
0
10 Jun 2024
Anomaly Detection via Federated Learning
Anomaly Detection via Federated LearningInternational Telecommunication Networks and Applications Conference (ITNAC), 2022
Marc Vucovich
A. Tarcar
Penjo Rebelo
N. Gade
Ruchi Porwal
...
R. Schiller
Edward Bowen
Alex West
Sanmitra Bhattacharya
Balaji Veeramani
FedML
144
16
0
12 Oct 2022
FedNorm: Modality-Based Normalization in Federated Learning for
  Multi-Modal Liver Segmentation
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation
Tobias Bernecker
Annette Peters
C. Schlett
F. Bamberg
Fabian J. Theis
Daniel Rueckert
J. Weiss
Shadi Albarqouni
FedMLMedIm
284
27
0
23 May 2022
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