Accelerating artificial intelligence: How federated learning can protect privacy, facilitate collaboration, and improve outcomes.

Malhar Patel¹, Ittai Dayan¹, Elliot K Fishman², Mona Flores³, Fiona J Gilbert⁴, Michal Guindy⁵, Eugene J Koay⁶, Michael Rosenthal⁷, Holger R Roth³, Marius G Linguraru⁸

¹Rhino Health, Boston, MA, USA.

²The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

³NVIDIA, Santa Clara, CA, USA.

⁴Department of Radiology, NIHR Cambridge Biomedical Resource Centre, University of Cambridge, Cambridge, CB, USA.

⁵Assuta Medical Centers, Tel Aviv, Israel; BGU University Israel, Beer-Sheva, Israel.

⁶Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

⁷Dana-Farber Cancer Institute, Boston, MA, USA; Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.

⁸Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA; Departments of Radiology and Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.

PubMed DOI

Abstract

Cross-institution collaborations are constrained by data-sharing challenges. These challenges hamper innovation, particularly in artificial intelligence, where models require diverse data to ensure strong performance. Federated learning (FL) solves data-sharing challenges. In typical collaborations, data is sent to a central repository where models are trained. With FL, models are sent to particip…

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