FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data
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Computer Science > Machine Learning
arXiv:2605.22954 (cs)
[Submitted on 21 May 2026]
Title:FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data
Authors:Maryam Moradpour, Jonas Harriehausen, Amirreza Aleyasin, Lion Philipp Wolf, Youngjun Park, Anne-Christin Hauschild
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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.
| Comments: | 4 pages, 2 figures. Maryam Moradpour, Jonas Harriehausen, and Amirreza Aleyasin contributed equally to this work. Includes supplementary material |
| Subjects: | Machine Learning (cs.LG); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2605.22954 [cs.LG] |
| (or arXiv:2605.22954v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22954
arXiv-issued DOI via DataCite (pending registration)
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