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@ARTICLE{WarnatHerresthal:155147,
author = {Warnat-Herresthal, Stefanie and Schultze, Hartmut and
Shastry, Krishnaprasad Lingadahalli and Manamohan,
Sathyanarayanan and Mukherjee, Saikat and Garg, Vishesh and
Sarveswara, Ravi and Händler, Kristian and Pickkers, Peter
and Aziz, Ahmad and Ktena, Sofia and Tran, Florian and
Bitzer, Michael and Ossowski, Stephan and Casadei, Nicolas
and Herr, Christian and Petersheim, Daniel and Behrends, Uta
and Kern, Fabian and Fehlmann, Tobias and Schommers, Philipp
and Lehmann, Clara and Augustin, Max and Rybniker, Jan and
Altmüller, Janine and Mishra, Neha and Bernardes, Joana P
and Krämer, Benjamin and Bonaguro, Lorenzo and
Schulte-Schrepping, Jonas and De Domenico, Elena and Siever,
Christian and Kraut, Michael and Desai, Milind and Monnet,
Bruno and Saridaki, Maria and Siegel, Charles Martin and
Drews, Anna and Nuesch Germano, Melanie and Theis, Heidi and
Heyckendorf, Jan and Schreiber, Stefan and Kim-Hellmuth,
Sarah and Nattermann, Jacob and Skowasch, Dirk and Kurth,
Ingo and Keller, Andreas and Bals, Robert and Nürnberg,
Peter and Rieß, Olaf and Rosenstiel, Philip and Netea,
Mihai G and Theis, Fabian and Mukherjee, Sach and Backes,
Michael and Aschenbrenner, Anna C and Ulas, Thomas and
Breteler, Monique and Giamarellos-Bourboulis, Evangelos J
and Kox, Matthijs and Becker, Matthias and Cheran, Sorin and
Woodacre, Michael S and Goh, Eng Lim and Schultze, Joachim L
and Balfanz, Paul and Eggermann, Thomas and Boor, Peter and
Hausmann, Ralf and Kuhn, Hannah and Isfort, Susanne and
Stingl, Julia Carolin and Schmalzing, Günther and Kuhl,
Christiane K and Röhrig, Rainer and Marx, Gernot and Uhlig,
Stefan and Dahl, Edgar and Müller-Wieland, Dirk and Dreher,
Michael and Marx, Nikolaus and Angelov, Angel and
Bartholomäus, Alexander and Becker, Anke and Bezdan,
Daniela and Blumert, Conny and Bonifacio, Ezio and Bork,
Peer and Boyke, Bunk and Blum, Helmut and Clavel, Thomas and
Colome-Tatche, Maria and Cornberg, Markus and De La Rosa
Velázquez, Inti Alberto and Diefenbach, Andreas and
Dilthey, Alexander and Fischer, Nicole and Förstner, Konrad
and Franzenburg, Sören and Frick, Julia-Stefanie and
Gabernet, Gisela and Gagneur, Julien and Ganzenmueller, Tina
and Gauder, Marie and Geißert, Janina and Goesmann,
Alexander and Göpel, Siri and Grundhoff, Adam and
Grundmann, Hajo and Hain, Torsten and Hanses, Frank and
Hehr, Ute and Heimbach, André and Hoeper, Marius and Horn,
Friedemann and Hübschmann, Daniel and Hummel, Michael and
Iftner, Thomas and Iftner, Angelika and Illig, Thomas and
Janssen, Stefan and Kalinowski, Jörn and Kallies, René and
Kehr, Birte and Keppler, Oliver T and Klein, Christoph and
Knop, Michael and Kohlbacher, Oliver and Köhrer, Karl and
Korbel, Jan and Kremsner, Peter G and Kühnert, Denise and
Landthaler, Markus and Li, Yang and Ludwig, Kerstin U and
Makarewicz, Oliwia and Marz, Manja and McHardy, Alice C and
Mertes, Christian and Münchhoff, Maximilian and Nahnsen,
Sven and Nöthen, Markus M. and Ntoumi, Francine and
Overmann, Jörg and Peter, Silke and Pfeffer, Klaus and
Pink, Isabell and Poetsch, Anna R and Protzer, Ulrike and
Pühler, Alfred and Rajewsky, Nikolaus and Ralser, Markus
and Reiche, Kristin and Ripke, Stephan and da Rocha, Ulisses
Nunes and Saliba, Antoine-Emmanuel and Sander, Leif Erik and
Sawitzki, Birgit and Scheithauer, Simone and Schiffer,
Philipp and Schmid-Burgk, Jonathan and Schneider, Wulf and
Schulte, Eva-Christina and Sczyrba, Alexander and Sharaf,
Mariam L and Singh, Yogesh and Sonnabend, Michael and
Stegle, Oliver and Stoye, Jens and Vehreschild, Janne and
Velavan, Thirumalaisamy P and Vogel, Jörg and Volland,
Sonja and von Kleist, Max and Walker, Andreas and Walter,
Jörn and Wieczorek, Dagmar and Winkler, Sylke and Ziebuhr,
John},
collaboration = {Study, COVID-19 Aachen and Initiative, Deutsche COVID-19
Omics},
title = {{S}warm {L}earning for decentralized and confidential
clinical machine learning.},
journal = {Nature},
volume = {594},
number = {7862},
issn = {1476-4687},
address = {London [u.a.]},
publisher = {Nature Publ. Group},
reportid = {DZNE-2021-00468},
pages = {265 - 270},
year = {2021},
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.},
keywords = {Blockchain / COVID-19: diagnosis / COVID-19: epidemiology /
Clinical Decision-Making: methods / Confidentiality /
Datasets as Topic / Disease Outbreaks / Female / Humans /
Leukemia: diagnosis / Leukemia: pathology / Leukocytes:
pathology / Lung Diseases: diagnosis / Machine Learning:
trends / Male / Precision Medicine: methods / Software /
Tuberculosis: diagnosis},
cin = {AG Aziz / AG Schultze / AG Breteler 1 / $R\&D$ PRECISE / AG
Fischer 1 / AG Mukherjee},
ddc = {500},
cid = {I:(DE-2719)5000071 / I:(DE-2719)1013031 /
I:(DE-2719)1012001 / I:(DE-2719)5000031 / I:(DE-2719)1410002
/ I:(DE-2719)1013030},
pnm = {354 - Disease Prevention and Healthy Aging (POF4-354) / 352
- Disease Mechanisms (POF4-352)},
pid = {G:(DE-HGF)POF4-354 / G:(DE-HGF)POF4-352},
experiment = {EXP:(DE-2719)Rhineland Study-20190321},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:34040261},
pmc = {pmc:PMC8189907},
doi = {10.1038/s41586-021-03583-3},
url = {https://pub.dzne.de/record/155147},
}
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