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Volume 594 Issue 7862, 10 June 2021

Swarm learning

A key element of precision medicine is rapid and reliable detection of patients who have a severe illness. Machine learning can help to achieve this, but implementing an effective system that maintains appropriate data confidentially is a challenge. In this week’s issue, Joachim Schultze and his colleagues show that swarm learning — a decentralized AI approach that unites edge computing, blockchain-based peer-to-peer networking and coordination — can be used to tackle the problem. The researchers used swarm learning to develop disease classifiers for leukaemia, COVID-19 and tuberculosis based on 16,400 blood transcriptomes and 95,000 chest X-rays. The classifiers outperformed those developed at individual medical facilities while fulfilling confidentiality regulations. The team notes that the distributed nature of swarm learning coupled with blockchain technology enables it to make use of large data sets without compromising on confidentiality.

Cover image: Concept: Stefanie Warnat-Herresthal/Matthias Becker; Design: Laura Lopez & Gianna Jaconelli (TBT Marketing Limited)/Stefanie Warnat-Herresthal.

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