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Federated machine learning in data-protection-compliant research

To fully leverage big data, they need to be shared across institutions in a manner compliant with privacy considerations and the EU General Data Protection Regulation (GDPR). Federated machine learning is a promising option.

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Fig. 1: Schematic representation of federated learning combined with privacy-enhancing techniques (PETs).


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Our work was funded by the German Federal Ministry of Education and Research (BMBF; grants 16DTM100A and 16DTM100C). We also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 826078. This publication reflects only the authors’ views, and the European Commission is not responsible for any use that may be made of the information it contains.

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Correspondence to Alissa Brauneck.

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Nature Machine Intelligence thanks Stuart McLennan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Brauneck, A., Schmalhorst, L., Kazemi Majdabadi, M.M. et al. Federated machine learning in data-protection-compliant research. Nat Mach Intell 5, 2–4 (2023).

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