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Swarm Learning as a privacy-preserving machine learning approach for disease classification

Stefanie Warnat-Herresthal, View ORCID ProfileHartmut Schultze, View ORCID ProfileKrishnaprasad Lingadahalli Shastry, View ORCID ProfileSathyanarayanan Manamohan, View ORCID ProfileSaikat Mukherjee, View ORCID ProfileVishesh Garg, Ravi Sarveswara, Kristian Händler, View ORCID ProfilePeter Pickkers, View ORCID ProfileN. Ahmad Aziz, Sofia Ktena, View ORCID ProfileChristian Siever, Michael Kraut, View ORCID ProfileMilind Desai, Bruno Monnet, View ORCID ProfileMaria Saridaki, View ORCID ProfileCharles Martin Siegel, Anna Drews, Melanie Nuesch-Germano, Heidi Theis, View ORCID ProfileMihai G. Netea, View ORCID ProfileFabian Theis, View ORCID ProfileAnna C. Aschenbrenner, View ORCID ProfileThomas Ulas, View ORCID ProfileMonique M.B. Breteler, View ORCID ProfileEvangelos J. Giamarellos-Bourboulis, View ORCID ProfileMatthijs Kox, View ORCID ProfileMatthias Becker, View ORCID ProfileSorin Cheran, Michael S. Woodacre, Eng Lim Goh, View ORCID ProfileJoachim L. Schultze, German COVID-19 OMICS Initiative (DeCOI)
doi: https://doi.org/10.1101/2020.06.25.171009
Stefanie Warnat-Herresthal
1Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, 53115 Bonn, Germany
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Hartmut Schultze
2Hewlett Packard Enterprise
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Krishnaprasad Lingadahalli Shastry
2Hewlett Packard Enterprise
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Sathyanarayanan Manamohan
2Hewlett Packard Enterprise
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Saikat Mukherjee
2Hewlett Packard Enterprise
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Vishesh Garg
2Hewlett Packard Enterprise
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Ravi Sarveswara
2Hewlett Packard Enterprise
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Kristian Händler
3German Center for Neurodegenerative Diseases (DZNE), PRECISE Platform for Single Cell Genomics and Epigenomics at DZNE and the University of Bonn, 53175 Bonn, Germany
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Peter Pickkers
4Department of Intensive Care Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, 6500HB, The Netherlands
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N. Ahmad Aziz
5Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), 53175 Bonn, Germany
6Department of Neurology, Faculty of Medicine, University of Bonn, 53127 Bonn, Germany
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Sofia Ktena
74th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, 124 62 Athens, Greece
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Christian Siever
2Hewlett Packard Enterprise
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Michael Kraut
3German Center for Neurodegenerative Diseases (DZNE), PRECISE Platform for Single Cell Genomics and Epigenomics at DZNE and the University of Bonn, 53175 Bonn, Germany
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Milind Desai
2Hewlett Packard Enterprise
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Bruno Monnet
2Hewlett Packard Enterprise
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Maria Saridaki
74th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, 124 62 Athens, Greece
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Charles Martin Siegel
2Hewlett Packard Enterprise
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Anna Drews
3German Center for Neurodegenerative Diseases (DZNE), PRECISE Platform for Single Cell Genomics and Epigenomics at DZNE and the University of Bonn, 53175 Bonn, Germany
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Melanie Nuesch-Germano
1Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, 53115 Bonn, Germany
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Heidi Theis
3German Center for Neurodegenerative Diseases (DZNE), PRECISE Platform for Single Cell Genomics and Epigenomics at DZNE and the University of Bonn, 53175 Bonn, Germany
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Mihai G. Netea
8Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen 6500HB, The Netherlands
9Immunology & Metabolism, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn 53115, Germany
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Fabian Theis
10Institute of Computational Biology, Helmholtz Center Munich (HMGU), 85764 Neuherberg, Germany
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Anna C. Aschenbrenner
1Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, 53115 Bonn, Germany
8Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen 6500HB, The Netherlands
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Thomas Ulas
3German Center for Neurodegenerative Diseases (DZNE), PRECISE Platform for Single Cell Genomics and Epigenomics at DZNE and the University of Bonn, 53175 Bonn, Germany
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Monique M.B. Breteler
5Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), 53175 Bonn, Germany
11Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53175 Bonn, Germany
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Evangelos J. Giamarellos-Bourboulis
74th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, 124 62 Athens, Greece
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  • ORCID record for Evangelos J. Giamarellos-Bourboulis
Matthijs Kox
4Department of Intensive Care Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, 6500HB, The Netherlands
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Matthias Becker
3German Center for Neurodegenerative Diseases (DZNE), PRECISE Platform for Single Cell Genomics and Epigenomics at DZNE and the University of Bonn, 53175 Bonn, Germany
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Sorin Cheran
2Hewlett Packard Enterprise
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Michael S. Woodacre
2Hewlett Packard Enterprise
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Eng Lim Goh
2Hewlett Packard Enterprise
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Joachim L. Schultze
1Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, 53115 Bonn, Germany
3German Center for Neurodegenerative Diseases (DZNE), PRECISE Platform for Single Cell Genomics and Epigenomics at DZNE and the University of Bonn, 53175 Bonn, Germany
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  • For correspondence: joachim.schultze@dzne.de
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Abstract

Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis and COVID-19 is an important goal of precision medicine. We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as privacy protection without the need for a central coordinator thereby going beyond federated learning. Using more than 14,000 blood transcriptomes derived from over 100 individual studies with non-uniform distribution of cases and controls and significant study biases, we illustrate the feasibility of SL to develop disease classifiers based on distributed data for COVID-19, tuberculosis or leukemias that outperform those developed at individual sites. Still, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.

Competing Interest Statement

H.S., K.L.S, S.Ma., S.Mu., V.G., R.S., C.S., M.D., B.M, C.M.S., S.C., M.S.W, E.L.G are employees of Hewlett-Packard Enterprise. Hewlett Packard Enterprise developed the Swarm Learning Library in its entirety as described in this work and has submitted multiple associated patent applications. E.J.G.-B. received honoraria from AbbVie USA, Abbott CH, InflaRx GmbH, MSD Greece, XBiotech Inc. and Angelini Italy; independent educational grants from AbbVie, Abbott, Astellas Pharma Europe, AxisShield, bioMerieux Inc, InflaRx GmbH, and XBiotech Inc.

Footnotes

  • ↵* shared first authors

  • ↵# shared last authors

  • Affiliation

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Swarm Learning as a privacy-preserving machine learning approach for disease classification
Stefanie Warnat-Herresthal, Hartmut Schultze, Krishnaprasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Händler, Peter Pickkers, N. Ahmad Aziz, Sofia Ktena, Christian Siever, Michael Kraut, Milind Desai, Bruno Monnet, Maria Saridaki, Charles Martin Siegel, Anna Drews, Melanie Nuesch-Germano, Heidi Theis, Mihai G. Netea, Fabian Theis, Anna C. Aschenbrenner, Thomas Ulas, Monique M.B. Breteler, Evangelos J. Giamarellos-Bourboulis, Matthijs Kox, Matthias Becker, Sorin Cheran, Michael S. Woodacre, Eng Lim Goh, Joachim L. Schultze, German COVID-19 OMICS Initiative (DeCOI)
bioRxiv 2020.06.25.171009; doi: https://doi.org/10.1101/2020.06.25.171009
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Swarm Learning as a privacy-preserving machine learning approach for disease classification
Stefanie Warnat-Herresthal, Hartmut Schultze, Krishnaprasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Händler, Peter Pickkers, N. Ahmad Aziz, Sofia Ktena, Christian Siever, Michael Kraut, Milind Desai, Bruno Monnet, Maria Saridaki, Charles Martin Siegel, Anna Drews, Melanie Nuesch-Germano, Heidi Theis, Mihai G. Netea, Fabian Theis, Anna C. Aschenbrenner, Thomas Ulas, Monique M.B. Breteler, Evangelos J. Giamarellos-Bourboulis, Matthijs Kox, Matthias Becker, Sorin Cheran, Michael S. Woodacre, Eng Lim Goh, Joachim L. Schultze, German COVID-19 OMICS Initiative (DeCOI)
bioRxiv 2020.06.25.171009; doi: https://doi.org/10.1101/2020.06.25.171009

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