  The correct interpretation of breast density is important in the assessment
of breast cancer risk. AI has been shown capable of accurately predicting
breast density, however, due to the differences in imaging characteristics
across mammography systems, models built using data from one system do not
generalize well to other systems. Though federated learning (FL) has emerged as
a way to improve the generalizability of AI without the need to share data, the
best way to preserve features from all training data during FL is an active
area of research. To explore FL methodology, the breast density classification
FL challenge was hosted in partnership with the American College of Radiology,
Harvard Medical School's Mass General Brigham, University of Colorado, NVIDIA,
and the National Institutes of Health National Cancer Institute. Challenge
participants were able to submit docker containers capable of implementing FL
on three simulated medical facilities, each containing a unique large
mammography dataset. The breast density FL challenge ran from June 15 to
September 5, 2022, attracting seven finalists from around the world. The
winning FL submission reached a linear kappa score of 0.653 on the challenge
test data and 0.413 on an external testing dataset, scoring comparably to a
model trained on the same data in a central location.
