这篇论文展示了联邦学习如何让两个不同规模、不同数据来源的医院协同训练模型，预测心血管病风险更准还保护隐私，值得医疗AI从业者看看。
该研究提出使用联邦深度学习进行隐私保护的心血管疾病风险预测，整合了两个异质性队列：Lifelines（148,230名参与者，自报结果）和Rotterdam Study（10,155名参与者，临床链接结果）。联邦训练的深度生存模型在Rotterdam Study上C-statistic从0.728升至0.739，在Lifelines上从0.783升至0.787，均优于本地训练模型。结果表明联邦学习能在不共享患者数据的前提下提升预测性能。
Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction
Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets. Extending these models across institutions could be limited by privacy regulations and constraints on sharing patient-level data. Federated learning enables collaborative model development without transferring sensitive patient data, but its application in healthcare remains challenging because datasets often differ in size, population characteristics, and outcome definitions. In this study, we present a federated deep learning approach for privacy-preserving cardiovascular disease risk prediction that integrates two population-based cohorts with different characteristics: Lifelines, including 148,230 participants meeting the study inclusion criteria with self-reported outcomes, and the Rotterdam Study, including a smaller cohort of 10,155 participants with digitally linked clinical outcomes. Model performance was primarily evaluated on the Rotterdam Study because of its complete follow-up. Deep survival models trained using federated learning achieved higher predictive performance than models trained locally without federation. For the Rotterdam Study, the C-statistic increased from 0.728 (95% CI: 0.717-0.739) to 0.739 (95% CI: 0.728-0.749). For Lifelines, the C-statistic increased from 0.783 (95% CI: 0.775-0.791) to 0.787 (95% CI: 0.780-0.792). These findings suggest that federated deep learning across heterogeneous cohorts can improve cardiovascular disease risk prediction while preserving the privacy of individual-level patient data.