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Federated machine learning in data-protection-compliant research

Nature Machine Intelligence volume 5, pages 2–4 (2023)Cite this article

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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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Acknowledgements

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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Author notes

  1. These authors contributed equally: Linda Baumbach, Gabriele Buchholtz.

Authors and Affiliations

  1. Hamburg University Faculty of Law, University of Hamburg, Hamburg, Germany

    Alissa Brauneck, Louisa Schmalhorst & Gabriele Buchholtz

  2. Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany

    Mohammad Mahdi Kazemi Majdabadi, Mohammad Bakhtiari, Christina Caroline Saak & Jan Baumbach

  3. Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany

    Uwe Völker

  4. Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Linda Baumbach

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  1. Alissa Brauneck
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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). https://doi.org/10.1038/s42256-022-00601-5

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