The generation of large amounts of healthcare data has motivated the use of Machine Learning (ML) to train robust models for clinical tasks. However, limitations of local datasets and restrictions on sharing patient data impede the use of traditional ML workflows. Consequently, Federated Learning (FL) has emerged as a potential solution for training ML models among multiple healthcare centers. In this study, we focus on the binary classification task of early ICU mortality prediction using Multivariate Time Series data and a deep neural network architecture. We evaluate the performance of two FL algorithms (FedAvg and FedProx) on this task, utilizing a real world multi-center benchmark database. Our results show that FL models outperform local ML models in a realistic scenario with non-identically distributed data, thus indicating that FL is a promising solution for analogous problems within the healthcare domain. Nevertheless, in this experimental scenario, they do not approximate the ideal performance of a centralized ML model.

The need to reduce energy consumption on a global scale has been of high importance during the last years. Research has created methods to make highly accurate forecasts on the energy consumption of buildings and there have been efforts towards the provision of automated forecasting for time series prediction problems. EnForce is a novel system that provides fully automatic forecasting on time series data, referring to the energy consumption of buildings. It uses statistical techniques and deep learning methods to make predictions on univariate or multivariate time series data, so that exogenous factors, such as outside temperature, are taken into account. Moreover, the proposed system provides automatic data preprocessing and, therefore, handles noisy data, with missing values and outliers. EnForce includes full API support and can be used both by experts and non-experts. The proposed demonstration showcases the advantages and technical features of EnForce.