  The amount of biomedical data continues to grow rapidly. However, the ability
to analyze these data is limited due to privacy and regulatory concerns.
Machine learning approaches that require data to be copied to a single location
are hampered by the challenges of data sharing. Federated Learning is a
promising approach to learn a joint model over data silos. This architecture
does not share any subject data across sites, only aggregated parameters, often
in encrypted environments, thus satisfying privacy and regulatory requirements.
Here, we describe our Federated Learning architecture and training policies. We
demonstrate our approach on a brain age prediction model on structural MRI
scans distributed across multiple sites with diverse amounts of data and
subject (age) distributions. In these heterogeneous environments, our
Semi-Synchronous protocol provides faster convergence.
