Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models

TL;DR

This paper proposes a federated learning approach for Cox proportional hazards models that addresses data heterogeneity and improves survival prediction accuracy across diverse healthcare datasets.

Contribution

It introduces feature-based clustering and event-based reporting strategies to enhance federated survival analysis, especially in heterogeneous healthcare data environments.

Findings

Improved model accuracy on synthetic datasets.

Effective adaptation to local data changes.

Potential for practical healthcare applications.

Abstract

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.