@article{3056276,
    title = "Swarm Learning for decentralized and confidential clinical machine learning",
    author = "Warnat-Herresthal, S. and Schultze, H. and Shastry, K.L. and Manamohan, S. and Mukherjee, S. and Garg, V. and Sarveswara, R. and Händler, K. and Pickkers, P. and Aziz, N.A. and Ktena, S. and Tran, F. and Bitzer, M. and Ossowski, S. and Casadei, N. and Herr, C. and Petersheim, D. and Behrends, U. and Kern, F. and Fehlmann, T. and Schommers, P. and Lehmann, C. and Augustin, M. and Rybniker, J. and Altmüller, J. and Mishra, N. and Bernardes, J.P. and Krämer, B. and Bonaguro, L. and Schulte-Schrepping, J. and De Domenico, E. and Siever, C. and Kraut, M. and Desai, M. and Monnet, B. and Saridaki, M. and Siegel, C.M. and Drews, A. and Nuesch-Germano, M. and Theis, H. and Heyckendorf, J. and Schreiber, S. and Kim-Hellmuth, S. and Balfanz, P. and Eggermann, T. and Boor, P. and Hausmann, R. and Kuhn, H. and Isfort, S. and Stingl, J.C. and Schmalzing, G. and Kuhl, C.K. and Röhrig, R. and Marx, G. and Uhlig, S. and Dahl, E. and Müller-Wieland, D. and Dreher, M. and Marx, N. and Nattermann, J. and Skowasch, D. and Kurth, I. and Keller, A. and Bals, R. and Nürnberg, P. and Rieß, O. and Rosenstiel, P. and Netea, M.G. and Theis, F. and Mukherjee, S. and Backes, M. and Aschenbrenner, A.C. and Ulas, T. and Angelov, A. and Bartholomäus, A. and Becker, A. and Bezdan, D. and Blumert, C. and Bonifacio, E. and Bork, P. and Boyke, B. and Blum, H. and Clavel, T. and Colome-Tatche, M. and Cornberg, M. and De La Rosa Velázquez, I.A. and Diefenbach, A. and Dilthey, A. and Fischer, N. and Förstner, K. and Franzenburg, S. and Frick, J.-S. and Gabernet, G. and Gagneur, J. and Ganzenmueller, T. and Gauder, M. and Geißert, J. and Goesmann, A. and Göpel, S. and Grundhoff, A. and Grundmann, H. and Hain, T. and Hanses, F. and Hehr, U. and Heimbach, A. and Hoeper, M. and Horn, F. and Hübschmann, D. and Hummel, M. and Iftner, T. and Iftner, A. and Illig, T. and Janssen, S. and Kalinowski, J. and Kallies, R. and Kehr, B. and Keppler, O.T. and Klein, C. and Knop, M. and Kohlbacher, O. and Köhrer, K. and Korbel, J. and Kremsner, P.G. and Kühnert, D. and Landthaler, M. and Li, Y. and Ludwig, K.U. and Makarewicz, O. and Marz, M. and McHardy, A.C. and Mertes, C. and Münchhoff, M. and Nahnsen, S. and Nöthen, M. and Ntoumi, F. and Overmann, J. and Peter, S. and Pfeffer, K. and Pink, I. and Poetsch, A.R. and Protzer, U. and Pühler, A. and Rajewsky, N. and Ralser, M. and Reiche, K. and Ripke, S. and da Rocha, U.N. and Saliba, A.-E. and Sander, L.E. and Sawitzki, B. and Scheithauer, S. and Schiffer, P. and Schmid-Burgk, J. and Schneider, W. and Schulte, E.-C. and Sczyrba, A. and Sharaf, M.L. and Singh, Y. and Sonnabend, M. and Stegle, O. and Stoye, J. and Vehreschild, J. and Velavan, T.P. and Vogel, J. and Volland, S. and von Kleist, M. and Walker, A. and Walter, J. and Wieczorek, D. and Winkler, S. and Ziebuhr, J. and Breteler, M.M.B. and Giamarellos-Bourboulis, E.J. and Kox, M. and Becker, M. and Cheran, S. and Woodacre, M.S. and Goh, E.L. and Schultze, J.L. and COVID-19 Aachen Study (COVAS) and Deutsche COVID-19 Omics Initiative (DeCOI)",
    journal = "Nature",
    year = "2021",
    volume = "594",
    number = "7862",
    pages = "265-270",
    publisher = "Institute of Geographic Sciences and Natural Resources Research",
    issn = "0028-0836",
    doi = "10.1038/s41586-021-03583-3",
    keywords = "transcriptome, blood;  computer simulation;  decentralization;  detection method;  feasibility study;  heterogeneity;  machine learning;  precision, Article;  blockchain based peer to peer coordination;  blockchain based peer to peer networking;  classifier;  confidential clinical machine learning;  coronavirus disease 2019;  decentralized machine learning;  edge computing;  feasibility study;  human;  information processing;  learning;  leukemia;  lung disease;  machine learning;  personalized medicine;  prediction;  Swarm Learning;  thorax radiography;  tuberculosis;  clinical decision making;  confidentiality;  epidemic;  epidemiology;  female;  information processing;  leukemia;  leukocyte;  lung disease;  male;  pathology;  procedures;  software, Blockchain;  Clinical Decision-Making;  Confidentiality;  COVID-19;  Datasets as Topic;  Disease Outbreaks;  Female;  Humans;  Leukemia;  Leukocytes;  Lung Diseases;  Machine Learning;  Male;  Precision Medicine;  Software;  Tuberculosis",
    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. © 2021, The Author(s)."
}