Scaling Survival Analysis in Healthcare with Federated Survival Forests:
  A Comparative Study on Heart Failure and Breast Cancer Genomics
Alberto Archetti Francesca Ieva Matteo Matteucci
Survival analysis is a fundamental tool in medicine, modeling the time until
an event of interest occurs in a population. However, in real-world
applications, survival data are often incomplete, censored, distributed, and
confidential, especially in healthcare settings where privacy is critical. The
scarcity of data can severely limit the scalability of survival models to
distributed applications that rely on large data pools. Federated learning is a
promising technique that enables machine learning models to be trained on
multiple datasets without compromising user privacy, making it particularly
well-suited for addressing the challenges of survival data and large-scale
survival applications. Despite significant developments in federated learning
for classification and regression, many directions remain unexplored in the
context of survival analysis. In this work, we propose an extension of the
Federated Survival Forest algorithm, called FedSurF++. This federated ensemble
method constructs random survival forests in heterogeneous federations.
Specifically, we investigate several new tree sampling methods from client
forests and compare the results with state-of-the-art survival models based on
neural networks. The key advantage of FedSurF++ is its ability to achieve
comparable performance to existing methods while requiring only a single
communication round to complete. The extensive empirical investigation results
in a significant improvement from the algorithmic and privacy preservation
perspectives, making the original FedSurF algorithm more efficient, robust, and
private. We also present results on two real-world datasets demonstrating the
success of FedSurF++ in real-world healthcare studies. Our results underscore
the potential of FedSurF++ to improve the scalability and effectiveness of
survival analysis in distributed settings while preserving user privacy.