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Swarm Learning for decentralized and confidential clinical machine learning

Author

Listed:
  • Stefanie Warnat-Herresthal

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    University of Bonn)

  • Hartmut Schultze

    (Hewlett Packard Enterprise)

  • Krishnaprasad Lingadahalli Shastry

    (Hewlett Packard Enterprise)

  • Sathyanarayanan Manamohan

    (Hewlett Packard Enterprise)

  • Saikat Mukherjee

    (Hewlett Packard Enterprise)

  • Vishesh Garg

    (Hewlett Packard Enterprise
    Mesh Dynamics)

  • Ravi Sarveswara

    (Hewlett Packard Enterprise)

  • Kristian Händler

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn)

  • Peter Pickkers

    (Radboud University Medical Center)

  • N. Ahmad Aziz

    (Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    Faculty of Medicine, University of Bonn)

  • Sofia Ktena

    (National and Kapodistrian University of Athens, Medical School)

  • Florian Tran

    (Christian-Albrechts-University and University Hospital Schleswig-Holstein
    Christian-Albrechts-University and University Hospital Schleswig-Holstein)

  • Michael Bitzer

    (University Hospital, University of Tübingen)

  • Stephan Ossowski

    (University of Tübingen
    NGS Competence Center Tübingen)

  • Nicolas Casadei

    (University of Tübingen
    NGS Competence Center Tübingen)

  • Christian Herr

    (Saarland University Hospital)

  • Daniel Petersheim

    (University Hospital LMU Munich)

  • Uta Behrends

    (Technical University Munich)

  • Fabian Kern

    (Saarland University)

  • Tobias Fehlmann

    (Saarland University)

  • Philipp Schommers

    (Faculty of Medicine and University Hospital of Cologne, University of Cologne)

  • Clara Lehmann

    (Faculty of Medicine and University Hospital of Cologne, University of Cologne
    University of Cologne
    Partner Site Bonn-Cologne)

  • Max Augustin

    (Faculty of Medicine and University Hospital of Cologne, University of Cologne
    University of Cologne
    Partner Site Bonn-Cologne)

  • Jan Rybniker

    (Faculty of Medicine and University Hospital of Cologne, University of Cologne
    University of Cologne
    Partner Site Bonn-Cologne)

  • Janine Altmüller

    (University of Cologne)

  • Neha Mishra

    (Christian-Albrechts-University and University Hospital Schleswig-Holstein)

  • Joana P. Bernardes

    (Christian-Albrechts-University and University Hospital Schleswig-Holstein)

  • Benjamin Krämer

    (Clinical Infectious Diseases, Research Center Borstel and German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems)

  • Lorenzo Bonaguro

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    University of Bonn)

  • Jonas Schulte-Schrepping

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    University of Bonn)

  • Elena Domenico

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn)

  • Christian Siever

    (Hewlett Packard Enterprise)

  • Michael Kraut

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn)

  • Milind Desai

    (Hewlett Packard Enterprise)

  • Bruno Monnet

    (Hewlett Packard Enterprise)

  • Maria Saridaki

    (National and Kapodistrian University of Athens, Medical School)

  • Charles Martin Siegel

    (Hewlett Packard Enterprise)

  • Anna Drews

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn)

  • Melanie Nuesch-Germano

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    University of Bonn)

  • Heidi Theis

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn)

  • Jan Heyckendorf

    (Clinical Infectious Diseases, Research Center Borstel and German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems)

  • Stefan Schreiber

    (Christian-Albrechts-University and University Hospital Schleswig-Holstein)

  • Sarah Kim-Hellmuth

    (University Hospital LMU Munich)

  • Jacob Nattermann

    (University Hospital Bonn
    German Center for Infection Research (DZIF))

  • Dirk Skowasch

    (University of Bonn)

  • Ingo Kurth

    (RWTH Aachen University)

  • Andreas Keller

    (Saarland University
    Stanford University School of Medicine)

  • Robert Bals

    (Saarland University Hospital)

  • Peter Nürnberg

    (University of Cologne)

  • Olaf Rieß

    (University of Tübingen
    NGS Competence Center Tübingen)

  • Philip Rosenstiel

    (Christian-Albrechts-University and University Hospital Schleswig-Holstein)

  • Mihai G. Netea

    (Radboud University Medical Center
    University of Bonn)

  • Fabian Theis

    (Helmholtz Center Munich (HMGU))

  • Sach Mukherjee

    (Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE))

  • Michael Backes

    (CISPA Helmholtz Center for Information Security)

  • Anna C. Aschenbrenner

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    University of Bonn
    PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn
    Radboud University Medical Center)

  • Thomas Ulas

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    University of Bonn)

  • Monique M. B. Breteler

    (Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    Faculty of Medicine, University of Bonn)

  • Evangelos J. Giamarellos-Bourboulis

    (National and Kapodistrian University of Athens, Medical School)

  • Matthijs Kox

    (Radboud University Medical Center)

  • Matthias Becker

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn)

  • Sorin Cheran

    (Hewlett Packard Enterprise)

  • Michael S. Woodacre

    (Hewlett Packard Enterprise)

  • Eng Lim Goh

    (Hewlett Packard Enterprise)

  • Joachim L. Schultze

    (Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
    University of Bonn
    PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn)

Abstract

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

Suggested Citation

  • Stefanie Warnat-Herresthal & Hartmut Schultze & Krishnaprasad Lingadahalli Shastry & Sathyanarayanan Manamohan & Saikat Mukherjee & Vishesh Garg & Ravi Sarveswara & Kristian Händler & Peter Pickkers &, 2021. "Swarm Learning for decentralized and confidential clinical machine learning," Nature, Nature, vol. 594(7862), pages 265-270, June.
  • Handle: RePEc:nat:nature:v:594:y:2021:i:7862:d:10.1038_s41586-021-03583-3
    DOI: 10.1038/s41586-021-03583-3
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    Citations

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    Cited by:

    1. Shivam Kalra & Junfeng Wen & Jesse C. Cresswell & Maksims Volkovs & H. R. Tizhoosh, 2023. "Decentralized federated learning through proxy model sharing," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Tao Qi & Fangzhao Wu & Chuhan Wu & Liang He & Yongfeng Huang & Xing Xie, 2023. "Differentially private knowledge transfer for federated learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

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