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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Auteurs principaux: 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
Langue:EN
Publié: Nature Portfolio 2021
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Accès en ligne:https://doaj.org/article/decac40d76f04fc18e73402b619ffedc
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