Swarm Learning for decentralized and confidential clinical machine learning

Abstract Fast and reliable detection of patients with severe heterogeneous illnesses is a major goal precision medicine 1,2 . Patients leukaemia can be identified using machine learning on the basis their blood transcriptomes 3 However, there an increasing divide between what technically possible allowed, because privacy legislation 4,5 Here, to facilitate integration any medical data from owner worldwide without violating laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking coordination while maintaining confidentiality need for central coordinator, thereby going beyond federated learning. To illustrate feasibility Learning develop disease classifiers distributed data, chose four use cases diseases (COVID-19, tuberculosis, lung pathologies). With more than 16,400 derived 127 clinical studies non-uniform distributions controls substantial study biases, as well 95,000 chest X-ray images, show outperform those developed at individual sites. In addition, completely fulfils local regulations by design. We believe this will notably accelerate introduction medicine.