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Swarm Learning for decentralized and confidential clinical machine learning

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posted on 2023-11-29, 18:06 authored by Michael Backes, et al
Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. 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.

History

Preferred Citation

Backes Michael and al et. Swarm Learning for decentralized and confidential clinical machine learning. In: Nature. 2021.

Primary Research Area

  • Trustworthy Information Processing

Legacy Posted Date

2021-05-27

Journal

Nature

Open Access Type

  • Gold

Sub Type

  • Article

BibTeX

@article{cispa_all_3399, title = "Swarm Learning for decentralized and confidential clinical machine learning", author = "Michael Backes and et al", journal="{Nature}", year="2021", }

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