  Electronic health record (EHR) data is collected by individual institutions
and often stored across locations in silos. Getting access to these data is
difficult and slow due to security, privacy, regulatory, and operational
issues. We show, using ICU data from 58 different hospitals, that machine
learning models to predict patient mortality can be trained efficiently without
moving health data out of their silos using a distributed machine learning
strategy. We propose a new method, called Federated-Autonomous Deep Learning
(FADL) that trains part of the model using all data sources in a distributed
manner and other parts using data from specific data sources. We observed that
FADL outperforms traditional federated learning strategy and conclude that
balance between global and local training is an important factor to consider
when design distributed machine learning methods , especially in healthcare.
