联邦随机生存森林解决多中心医疗数据部分重叠下的隐私保护生存预测问题。
FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data
- 基于本地训练的生存树聚合，仅共享特征兼容的树结构。
- 在乳腺癌数据上达到与集中训练相当的预测性能（C-Index）。
- 适合需要隐私保护且特征不完全一致的医疗协作场景。
多中心生存预测可提升模型鲁棒性和泛化能力，但隐私法规和机构治理常阻碍患者级临床与基因组数据跨机构汇聚。实践中，因各机构采集不同协变量或使用不同测序面板，导致特征空间存在部分重叠。我们提出FederatedRSF，一个基于Python的联邦随机生存森林实现框架，通过聚合本地训练的生存树，并仅向各机构分发特征兼容的树结构，实现无需共享原始数据的推理。我们在GBSG2乳腺癌队列上评估该方法，利用scikit-survival包提供的数据，通过剔除部分特征模拟客户端间特征异质性，并在重复交叉验证与站点分割下以Harrell's C-Index评估判别能力。结果表明，联邦模型性能可媲美集中式训练设置。
原文摘要 · Abstract (English)
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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