Federated Data Sharing
Federated Data Sharing is a secure data sharing paradigm in which multiple entities collaboratively access and analyze distributed datasets without transferring raw data, preserving data sovereignty and privacy by keeping data localized while enabling integrated query processing or model training across decentralized sources.
112
Publications
7.3K
Citations
687
Authors
316
Institutions
Publications per year
2017–2026
93
112
Federated Learning for Healthcare Informatics
Jie Xu, Benjamin S. Glicksberg, Chang Su et al. · Journal of Healthcare Informatics Research · 2020 · 1.4K citations · Full text
1–5 of 112
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1–5 of 687
316

Beijing University of Posts and Telecommunications
Beijing, China
4 h-index
15 publications
555 citations

Xidian University
Xi'an, China
4 h-index
10 publications
282 citations

The University of Melbourne
Melbourne, Australia
3 h-index
6 publications
114 citations

University of Novi Sad
Novi Sad, Serbia
3 h-index
8 publications
158 citations
Fujian Normal University
Fuzhou, China
3 h-index
7 publications
309 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
3 h-index
4 publications
117 citations

Cornell University
Ithaca, United States
3 h-index
8 publications
4.5K citations
University of Southern California
Los Angeles, United States
3 h-index
4 publications
602 citations
1–8 of 316
8 publications · 649 citations
8
h-index
Repository · 6 publications · 208 citations
6
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5 publications · 614 citations
5
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Repository · 3 publications · 58 citations
3
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JAMIA · 3 publications · 214 citations
3
h-index
1–5