Horizontal federated learning and assessment of Cox models
article
The Cox Proportional Hazards model is a widely used method for survival analysis in medical research. However, training an accurate model requires access to a sufficiently large dataset, which is often challenging due to data fragmentation. A potential solution is to combine data from multiple medical institutions, but privacy constraints typically prevent direct data sharing. Federated learning offers a privacy-preserving alternative by allowing multiple parties to collaboratively train a model without exchanging raw data. In this work, we develop algorithms for training Cox models in a federated setting, leveraging survival stacking to facilitate distributed learning. In addition, we introduce a novel secure computation of Schoenfeld residuals, a key diagnostic tool for validating the Cox model. We provide an open-source implementation of our approach and present empirical results that demonstrate the accuracy and benefits of federated Cox regression. 2025 Westers, Leder and Tealdi.
Topics
TNO Identifier
1015909
ISSN
2673253X
Source
Frontiers in Digital Health, 7
Publisher
Frontiers Media SA
Article nr.
160363