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Comparison of Privacy-Preserving Distributed Deep Learning Methods in
  Healthcare

Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare

23 December 2020
M. Gawali
S. ArvindC.
Shriya Suryavanshi
Harshit Madaan
A. Gaikwad
KN BhanuPrakash
V. Kulkarni
Aniruddha Pant
    FedML
ArXivPDFHTML

Papers citing "Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare"

5 / 5 papers shown
Title
Parallel Split Learning with Global Sampling
Parallel Split Learning with Global Sampling
Mohammad Kohankhaki
Ahmad Ayad
Mahdi Barhoush
A. Schmeink
38
1
0
22 Jul 2024
Combined Federated and Split Learning in Edge Computing for Ubiquitous
  Intelligence in Internet of Things: State of the Art and Future Directions
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions
Qiang Duan
Shijing Hu
Ruijun Deng
Zhihui Lu
FedML
31
61
0
20 Jul 2022
Enabling All In-Edge Deep Learning: A Literature Review
Enabling All In-Edge Deep Learning: A Literature Review
Praveen Joshi
Mohammed Hasanuzzaman
Chandra Thapa
Haithem Afli
T. Scully
25
22
0
07 Apr 2022
Robust and Privacy-Preserving Collaborative Learning: A Comprehensive
  Survey
Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey
Shangwei Guo
Xu Zhang
Feiyu Yang
Tianwei Zhang
Yan Gan
Tao Xiang
Yang Liu
FedML
31
9
0
19 Dec 2021
The Future of Digital Health with Federated Learning
The Future of Digital Health with Federated Learning
Nicola Rieke
Jonny Hancox
Wenqi Li
Fausto Milletari
H. Roth
...
Ronald M. Summers
Andrew Trask
Daguang Xu
Maximilian Baust
M. Jorge Cardoso
OOD
174
1,707
0
18 Mar 2020
1