Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data.

ABSTRACT: Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achi

SUBMITTER: Sheller MJ 

PROVIDER: S-EPMC7387485 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature