Federated Transfer Learning via Random Forests for Survival Analysis

Thomas M. Menino Convention & Exhibition Center 

Survival analysis in biomedical research is constrained by data scarcity, inter-institutional heterogeneity, and privacy regulations that prohibit centralized data sharing. Federated learning preserves data locality but typically focuses on learning a single global model and does not directly address limited target-site sample sizes. Transfer learning can mitigate this limitation, yet many existing methods require access to source data or iterative communication. We propose Federated Transfer Learning via Random Survival Forests (FTRSF), a privacy-preserving and communication-efficient framework for decentralized time-to-event analysis. FTRSF integrates federated and transfer learning by training models locally at auxiliary sites and transferring knowledge asymmetrically to a designated target site through pseudo-features derived from auxiliary risk scores. This one-shot approach accommodates censoring and complex risk structures without sharing individual-level data. Simulation studies and a real-data application show that FTRSF improves target-site prediction accuracy, particularly for small or heavily censored cohorts, while remaining robust to moderate inter-site heterogeneity.

Confidential data

Decentralized data

Distributed learning

Model sharing

Random survival forests 

Biometrics Section