Privacy-first health research with federated learning

Privacy-first health research with federated learning

Abstract Privacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned m...

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Bibliographic Details
Main Authors: Adam Sadilek, Luyang Liu, Dung Nguyen, Methun Kamruzzaman, Stylianos Serghiou, Benjamin Rader, Alex Ingerman, Stefan Mellem, Peter Kairouz, Elaine O. Nsoesie, Jamie MacFarlane, Anil Vullikanti, Madhav Marathe, Paul Eastham, John S. Brownstein, Blaise Aguera y. Arcas, Michael D. Howell, John Hernandez
Format: article
Language:EN
Published: Nature Portfolio 2021
Subjects:
Computer applications to medicine. Medical informatics
R858-859.7
Online Access:https://doaj.org/article/decac40d76f04fc18e73402b619ffedc
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https://doaj.org/article/decac40d76f04fc18e73402b619ffedc

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