Federated learning is not a cure-all for data ethics

Marieke Bak, Vince I. Madai, Leo Anthony Celi, Georgios A. Kaissis, Ronald Cornet, Menno Maris, Daniel Rueckert, Alena Buyx, Stuart McLennan

Research output: Contribution to journalComment/Letter to the editorAcademic


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.

Original languageEnglish
Pages (from-to)370-372
Number of pages3
JournalNature Machine Intelligence
Issue number4
Early online date2024
Publication statusPublished - Apr 2024

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