  Leveraging real-world health data for machine learning tasks requires
addressing many practical challenges, such as distributed data silos, privacy
concerns with creating a centralized database from person-specific sensitive
data, resource constraints for transferring and integrating data from multiple
sites, and risk of a single point of failure. In this paper, we introduce a
federated learning framework that can learn a global model from distributed
health data held locally at different sites. The framework offers two levels of
privacy protection. First, it does not move or share raw data across sites or
with a centralized server during the model training process. Second, it uses a
differential privacy mechanism to further protect the model from potential
privacy attacks. We perform a comprehensive evaluation of our approach on two
healthcare applications, using real-world electronic health data of 1 million
patients. We demonstrate the feasibility and effectiveness of the federated
learning framework in offering an elevated level of privacy and maintaining
utility of the global model.
