Swarm Learning for decentralized and confidential clinical machine learning
Authors: 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 ,   COVID-19 Aachen Study (COVAS), Balfanz Paul , Eggermann Thomas , Boor Peter , Hausmann Ralf , Kuhn Hannah , Isfort Susanne , Stingl Julia Carolin , Schmalzing Günther , Kuhl Christiane K. , Röhrig Rainer , Marx Gernot , Uhlig Stefan , Dahl Edgar , Müller-Wieland Dirk , Dreher Michael , Marx Nikolaus , 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 ,   Deutsche COVID-19 Omics Initiative (DeCOI), Angelov Angel , Bartholomäus Alexander , Becker Anke , Bezdan Daniela , Blumert Conny , Bonifacio Ezio , Bork Peer , Boyke Bunk , Blum Helmut , Clavel Thomas , Colome-Tatche Maria , Cornberg Markus , De La Rosa Velázquez Inti Alberto , Diefenbach Andreas , Dilthey Alexander , Fischer Nicole , Förstner Konrad , Franzenburg Sören , Frick Julia-Stefanie , Gabernet Gisela , Gagneur Julien , Ganzenmueller Tina , Gauder Marie , Geißert Janina , Goesmann Alexander , Göpel Siri , Grundhoff Adam , Grundmann Hajo , Hain Torsten , Hanses Frank , Hehr Ute , Heimbach André , Hoeper Marius , Horn Friedemann , Hübschmann Daniel , Hummel Michael , Iftner Thomas , Iftner Angelika , Illig Thomas , Janssen Stefan , Kalinowski Jörn , Kallies René , Kehr Birte , Keppler Oliver T. , Klein Christoph , Knop Michael , Kohlbacher Oliver , Köhrer Karl , Korbel Jan , Kremsner Peter G. , Kühnert Denise , Landthaler Markus , Li Yang , Ludwig Kerstin U. , Makarewicz Oliwia , Marz Manja , McHardy Alice C. , Mertes Christian , Münchhoff Maximilian , Nahnsen Sven , Nöthen Markus , Ntoumi Francine , Overmann Jörg , Peter Silke , Pfeffer Klaus , Pink Isabell , Poetsch Anna R. , Protzer Ulrike , Pühler Alfred , Rajewsky Nikolaus , Ralser Markus , Reiche Kristin , Ripke Stephan , da Rocha Ulisses Nunes , Saliba Antoine-Emmanuel , Sander Leif Erik , Sawitzki Birgit , Scheithauer Simone , Schiffer Philipp , Schmid-Burgk Jonathan , Schneider Wulf , Schulte Eva-Christina , Sczyrba Alexander , Sharaf Mariam L. , Singh Yogesh , Sonnabend Michael , Stegle Oliver , Stoye Jens , Vehreschild Janne , Velavan Thirumalaisamy P. , Vogel Jörg , Volland Sonja , von Kleist Max , Walker Andreas , Walter Jörn , Wieczorek Dagmar , Winkler Sylke , Ziebuhr John , Breteler Monique M. B. , Giamarellos-Bourboulis Evangelos J. , Kox Matthijs , Becker Matthias , Cheran Sorin , Woodacre Michael S. , Goh Eng Lim , Schultze Joachim L. 
Published: May 2021

AbstractFast 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.