Please use this identifier to cite or link to this item: doi:10.22028/D291-36759
Title: Swarm Learning for decentralized and confidential clinical machine learning
Author(s): Warnat-Herresthal, Stefanie
Schultze, Hartmut
Shastry, Krishnaprasad Lingadahalli
Manamohan, Sathyanarayanan
Mukherjee, Saikat
Garg, Vishesh
Sarveswara, Ravi
Händler, Kristian
Pickkers, Peter
Aziz, N. Ahmad
Ktena, Sofia
Tran, Florian
Bitzer, Michael
Ossowski, Stephan
Casadei, Nicolas
Herr, Christian
Petersheim, Daniel
Behrends, Uta
Kern, Fabian
Fehlmann, Tobias
Schommers, Philipp
Lehmann, Clara
Augustin, Max
Rybniker, Jan
Altmüller, Janine
Mishra, Neha
Bernardes, Joana P.
Krämer, Benjamin
Bonaguro, Lorenzo
Schulte-Schrepping, Jonas
De Domenico, Elena
Siever, Christian
Kraut, Michael
Desai, Milind
Monnet, Bruno
Saridaki, Maria
Siegel, Charles Martin
Drews, Anna
Nuesch-Germano, Melanie
Theis, Heidi
Heyckendorf, Jan
Schreiber, Stefan
Kim-Hellmuth, Sarah
Nattermann, Jacob
Skowasch, Dirk
Kurth, Ingo
Keller, Andreas
Bals, Robert
Nürnberg, Peter
Rieß, Olaf
Rosenstiel, Philip
Netea, Mihai G.
Theis, Fabian
Mukherjee, Sach
Backes, Michael
Aschenbrenner, Anna C.
Ulas, Thomas
Breteler, Monique M. B.
Giamarellos-Bourboulis, Evangelos J.
Kox, Matthijs
Becker, Matthias
Cheran, Sorin
Woodacre, Michael S.
Goh, Eng Lim
Schultze, Joachim L.
Language: English
Title: Nature
Volume: 594
Issue: 7862
Pages: 265–270
Publisher/Platform: Springer Nature
Year of Publication: 2021
Free key words: Computational models
Diagnostic markers
Machine learning
Predictive medicine
Viral infection
DDC notations: 610 Medicine and health
Publikation type: Journal Article
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 identifed 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 confdentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifers 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 classifers outperform those developed at individual sites. In addition, Swarm Learning completely fulfls local confdentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.
DOI of the first publication: 10.1038/s41586-021-03583-3
URL of the first publication:
Link to this record: urn:nbn:de:bsz:291--ds-367598
ISSN: 1476-4687
Date of registration: 11-Jul-2022
Description of the related object: Supplementary information
Related object:
Faculty: M - Medizinische Fakultät
Department: M - Innere Medizin
M - Medizinische Biometrie, Epidemiologie und medizinische Informatik
Professorship: M - Prof. Dr. Robert Bals
M - Univ.-Prof. Dr. Andreas Keller
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

Files for this record:
File Description SizeFormat 
s41586-021-03583-3.pdf12,56 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons