Meeting KI-Postdocs: ‘Swarm Learning' Full Title: "Swarm Learning as a fully decentralized and confidentiality-enabling machine learning approach for disease classification" (Prof. Dr. Joachim Schultze & Stefanie Warnat-Herresthal) Fast and reliable detection of patients with severe and heterogenous illnesses is a major goal of precision medicine. We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any medical data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as confidentiality protection without the need for a central coordinator thereby going beyond federated learning. https://www.uni-bonn.de/en/research-and-teaching/research-profile/transdisciplinary-research-areas/tra-4-individuals/events-1/copy5_of_what-machines-shouldnt-do-part-of-the-ai-research-group https://www.uni-bonn.de/++resource++plone-logo.svg

Full Title: "Swarm Learning as a fully decentralized and confidentiality-enabling machine learning approach for disease classification"
(Prof. Dr. Joachim Schultze & Stefanie Warnat-Herresthal)

Fast and reliable detection of patients with severe and heterogenous illnesses is a major goal of precision medicine. We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any medical data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as confidentiality protection without the need for a central coordinator thereby going beyond federated learning.