  Building robust deep learning-based models requires large quantities of
diverse training data. In this study, we investigate the use of federated
learning (FL) to build medical imaging classification models in a real-world
collaborative setting. Seven clinical institutions from across the world joined
this FL effort to train a model for breast density classification based on
Breast Imaging, Reporting & Data System (BI-RADS). We show that despite
substantial differences among the datasets from all sites (mammography system,
class distribution, and data set size) and without centralizing data, we can
successfully train AI models in federation. The results show that models
trained using FL perform 6.3% on average better than their counterparts trained
on an institute's local data alone. Furthermore, we show a 45.8% relative
improvement in the models' generalizability when evaluated on the other
participating sites' testing data.
