  The diversity in disease profiles and therapeutic approaches between
hospitals and health professionals underscores the need for patient-centric
personalized strategies in healthcare. Alongside this, similarities in disease
progression across patients can be utilized to improve prediction models in
survival analysis. The need for patient privacy and the utility of prediction
models can be simultaneously addressed in the framework of Federated Learning
(FL). This paper outlines an approach in the domain of federated survival
analysis, specifically the Cox Proportional Hazards (CoxPH) model, with a
specific focus on mitigating data heterogeneity and elevating model
performance. We present an FL approach that employs feature-based clustering to
enhance model accuracy across synthetic datasets and real-world applications,
including the Surveillance, Epidemiology, and End Results (SEER) database.
Furthermore, we consider an event-based reporting strategy that provides a
dynamic approach to model adaptation by responding to local data changes. Our
experiments show the efficacy of our approach and discuss future directions for
a practical application of FL in healthcare.
