  Federated learning enables training a global machine learning model from data
distributed across multiple sites, without having to move the data. This is
particularly relevant in healthcare applications, where data is rife with
personal, highly-sensitive information, and data analysis methods must provably
comply with regulatory guidelines. Although federated learning prevents sharing
raw data, it is still possible to launch privacy attacks on the model
parameters that are exposed during the training process, or on the generated
machine learning model. In this paper, we propose the first syntactic approach
for offering privacy in the context of federated learning. Unlike the
state-of-the-art differential privacy-based frameworks, our approach aims to
maximize utility or model performance, while supporting a defensible level of
privacy, as demanded by GDPR and HIPAA. We perform a comprehensive empirical
evaluation on two important problems in the healthcare domain, using real-world
electronic health data of 1 million patients. The results demonstrate the
effectiveness of our approach in achieving high model performance, while
offering the desired level of privacy. Through comparative studies, we also
show that, for varying datasets, experimental setups, and privacy budgets, our
approach offers higher model performance than differential privacy-based
techniques in federated learning.
