Life-threatening conditions like sepsis are a leading cause of hospital mortality. The early identification of sepsis onset allows for timely intervention aiming to save patient lives. Although showing great promise for early sepsis onset prediction, Centralized Machine Learning applications are hindered by privacy concerns. Federated Learning has the potential to counteract the mentioned limitation as it trains a global model utilizing distributed data across several hospitals without sharing the data. This research explores the potential of Federated Learning to provide a more privacy-preserving and generalizable solution for predicting sepsis onset using a Deep Federated Learning setup. Patients from the MIMIC-III dataset are classified as either septic or non-septic using relevant patient features, and sepsis onset is identified at the first hour of a detected 5-hour SIRS interval for patients diagnosed with sepsis. We compare the predictive performance of different combinations of classifiers (LSTM and GRU), patient history window lengths, prediction window lengths, and Federated Learning clients, using the metrics AUROC, AUPRC, and F1-Score. Our results show that the Centralized Machine Learning and Federated Learning setups are on par in terms of predictive performance. In addition, on average, the best-performing Federated Learning model is GRU, with a five-hour patient history window and a three-hour prediction window. Overall, the study demonstrates that the proposed Federated Learning setup can predict sepsis onset comparably to state-of-the-art centralized deep learning algorithms for varying numbers of clients, enabling healthcare institutions to collaborate on mutually beneficial tasks without sharing isolated sensitive patient information.