arXiv:2605.22954v1 Announce Type: new 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 surv
The increasing pressure for data privacy combined with the growing need for robust AI models in healthcare is driving innovations in federated learning solutions.
This development allows for powerful AI integration in sensitive sectors like healthcare without compromising patient privacy or violating institutional data governance.
The ability to train AI models across heterogeneous, partially overlapping datasets in a federated manner enhances model generalizability and utility in real-world clinical settings.
- · Healthcare institutions
- · AI/ML developers
- · Patients
- · Biomedical research
- · Data intermediaries reliant on centralized data pooling
- · Traditional, non-federated AI model developers
Improved accuracy and robustness of medical AI models across diverse patient populations.
Accelerated development of personalized medicine due to broader data access without compromising privacy.
Potential for new 'data-sharing' economies based on federated access rather than direct data exchange, redefining data ownership and value.
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Read at arXiv cs.LG