Journal

The Future of Digital Health with Federated Learning

Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletarì, Holger R. Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus Maier-Hein, Sébastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso

Nature npj Digital Medicine, September 2020

PublisherPDFInteractive explainer · soonBibTeX
Teaser figure

Abstract

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how Federated Learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.

Interactive explainer

Coming soon. An interactive Slidev walkthrough of this paper — with live, explorable figures — will appear here. Authors: set slidev: in this page's front matter to the deployed deck URL.

Research themes

Medical ImagingResponsible AIFederated Learning

Funded by

Deep Federated Learning in …

Keywords

Federated Learning

Lab authors

Nicola RiekeShadi Albarqouni
Cite (BibTeX)
@article{rieke2020future,
 author = {Rieke, Nicola and Hancox, Jonny and Li, Wenqi and Milletari, Fausto and Roth, Holger and Albarqouni, Shadi and Bakas, Spyridon and Galtier, Mathieu N and Landman, Bennett and Maier-Hein, Klaus and others},
 journal = {arXiv preprint arXiv:2003.08119},
 title = {The Future of Digital Health with Federated Learning},
 year = {2020}
}