Prediction of Hospital Readmission using Federated Learning

Wearable devices have the ability to generate vast amounts of data that can be put to use in a multitude of applications, particularly in the field of e-health. However, the potential invasion of privacy that comes with utilizing personal data collected by these devices cannot be overlooked. Federated Learning (FL) is a promising solution to this issue that allows models to be trained in a decentralized manner while keeping user data on their own devices. This approach effectively minimizes the risk of privacy breaches and has the potential to be employed in a variety of applications where the protection of user data is of utmost importance. This paper focuses on the use of FL in predicting hospital readmission in 130-US diabetes hospitals for a data set collected over an 9-year period. The results suggest that FL can achieve comparable performance while maintaining privacy and diversity of data. This is an essential aspect of FL, as it enables continuous real-time learning without compromising privacy.