  Machine learning models can be used for pattern recognition in medical data
in order to improve patient outcomes, such as the prediction of in-hospital
mortality. Deep learning models, in particular, require large amounts of data
for model training. However, the data is often collected at different hospitals
and sharing is restricted due to patient privacy concerns. In this paper, we
aimed to demonstrate the potential of distributed training in achieving
state-of-the-art performance while maintaining data privacy. Our results show
that training the model in the federated learning framework leads to comparable
performance to the traditional centralised setting. We also suggest several
considerations for the success of such frameworks in future work.
