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Leveraging real-world health data for machine learning tasks requiresaddressing many practical challenges, such as distributed data silos, privacyconcerns with creating a centralized database from person-specific sensitivedata, resource constraints for transferring and integrating data from multiplesites, and risk of a single point of failure. In this paper, we introduce afederated learning framework that can learn a global model from distributedhealth data held locally at different sites. The framework offers two levels ofprivacy protection. First, it does not move or share raw data across sites orwith a centralized server during the model training process. Second, it uses adifferential privacy mechanism to further protect the model from potentialprivacy attacks. We perform a comprehensive evaluation of our approach on twohealthcare applications, using real-world electronic health data of 1 millionpatients. We demonstrate the feasibility and effectiveness of the federatedlearning framework in offering an elevated level of privacy and maintainingutility of the global model.

Differential Privacy-enabled Federated Learning for Sensitive Health Data