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

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…

• NI
  National Institutes of Health

  Awards: CCSG P30CA047904, NINDS:R01NS042645, NCI:U01CA242871, U24CA189523, U01CA242871, P30CA047904, NCI:U24CA189523, R01NS042645
• NC
  National Cancer Institute

  Awards: P30CA047904, R01NS042645, U24CA189523, U01CA242871, CCSG P30CA047904
• NI
  National Institute of Neurological Disorders and Stroke

  Awards: R01NS042645, NINDS:R01NS042645, U01CA242871, U24CA189523