FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data

• URL: http://arxiv.org/abs/2605.22954v1
• Date: Thu, 21 May 2026 18:32:25 GMT
• Title: FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data
• Abstract summary: FederatedRSF implements random survival forests, aggregating locally trained survival trees and redistributing only feature-compatible trees to each site.<n>We evaluate FederatedRSF on the GBSG2 breast cancer cohort distributed with the scikit-survival package.
• Score: 1.0303353919699785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-center survival prediction can improve robustness and generalizability, yet privacy regulations and institutional governance often prevent pooling patient-level clinical and genomic data across institutions. In practice, deployment is further complicated by feature-space heterogeneity, in which sites collect different covariates or use different sequencing panels, resulting in only partially overlapping feature sets. We present FederatedRSF, a Python package that implements federated random survival forests, aggregating locally trained survival trees and redistributing only feature-compatible trees to each site, enabling inference with partial overlap without sharing raw data. We evaluate FederatedRSF on the GBSG2 breast cancer cohort distributed with the scikit-survival package, simulating feature heterogeneity across clients by withholding subsets of features, and assessing discrimination using Harrell's concordance index (C-Index) under repeated cross-validation and site-splits. The results demonstrated that the federated model can achieve performance comparable to that of the centralized training setting.

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