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Bak, M.* ; Madai, V.I.* ; Celi, L.A.* ; Kaissis, G. ; Cornet, R.* ; Maris, M.* ; Rueckert, D.* ; Buyx, A.* ; McLennan, S.*

Federated learning is not a cure-all for data ethics.

Nat. Mach. Intell. 6, 370–372 (2024)
DOI
Although federated learning is often seen as a promising solution to allow AI innovation while addressing privacy concerns, we argue that this technology does not fix all underlying data ethics concerns. Benefiting from federated learning in digital health requires acknowledgement of its limitations.
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Publication type Article: Journal article
Document type Letter to the Editor
Corresponding Author
ISSN (print) / ISBN 2522-5839
e-ISSN 2522-5839
Journal Nature machine intelligence
Quellenangaben Volume: 6, Issue: , Pages: 370–372 Article Number: , Supplement: ,
Publisher Springer
Publishing Place [London]
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)