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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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Letter to the Editor
Korrespondenzautor
ISSN (print) / ISBN 2522-5839
e-ISSN 2522-5839
Zeitschrift Nature machine intelligence
Quellenangaben Band: 6, Heft: , Seiten: 370–372 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort [London]
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)