Privacy preservation for federated learning in health care

Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns. Federated learning (FL) allows for multi-institutional training of AI models, obviating data sharing, albeit with different security and privacy concerns. Specifically, insights exchanged during FL can leak information about institutional data. In addition, FL can introduce issues when there is limited trust among the entities performing the compute. With the growing…

• IU
  Indiana University
• UO
  University of Pennsylvania
• NI
  National Institutes of Health

  Awards: U01CA242871, U01CA242879
• PS
  Perelman School of Medicine, University of Pennsylvania
• NC
  National Cancer Institute

  Awards: U24CA279629, U01CA242879, U01CA242871
• DO
  Department of Radiology, Weill Cornell Medical College