Federatedlearning

Federated learning and Digital Health

In 2016, McMahan and colleagues at Google published a paper on a decentralized approach to machine learning and coined the term “federated learning” to describe a process in which model parameters are aggregated from individual clients that run local training sets, while leaving individual data on local devices. This concept is currently being deployed in health care to address challenges of interoperability, data governance, and data bias in

References:

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20 Th International Conference on Artificial Intelligence and Statistics (AISTATS), Feb, 1–42. arxiv:1602.05629

Rieke, Nicola; Hancox, Jonny; Li, Wenqi; Milletarì, Fausto; Roth, Holger R.; Albarqouni, Shadi; Bakas, Spyridon; Galtier, Mathieu N.; Landman, Bennett A.; Maier-Hein, Klaus; Ourselin, Sébastien; Sheller, Micah; Summers, Ronald M.; Trask, Andrew; Xu, Daguang; Baust, Maximilian; Cardoso, M. Jorge (14 September 2020). "The future of digital health with federated learning". NPJ Digital Medicine. 3 (1): 119. arXiv:2003.08119. doi:10.1038/s41746-020-00323-1. PMC 7490367. PMID 33015372. S2CID 212747909.