The diversity in disease profiles and therapeutic approaches between hospitals and health professionals underscores the need for patient-centric personalized strategies in healthcare. Alongside this, similarities in disease progression across patients can be utilized to improve prediction models in survival analysis. The need for patient privacy and the utility of prediction models can be simultaneously addressed in the framework of Federated Learning (FL). This paper outlines an approach in the domain of federated survival analysis, specifically the Cox Proportional Hazards (CoxPH) model, with a specific focus on mitigating data heterogeneity and elevating model performance. We present an FL approach that employs feature-based clustering to enhance model accuracy across synthetic datasets and real-world applications, including the Surveillance, Epidemiology, and End Results (SEER) database. Furthermore, we consider an event-based reporting strategy that provides a dynamic approach to model adaptation by responding to local data changes. Our experiments show the efficacy of our approach and discuss future directions for a practical application of FL in healthcare.

翻译：医院与医疗专业人员之间疾病特征和治疗方法的多样性凸显了医疗领域需要以患者为中心的个性化策略。与此同时，患者间疾病进展的相似性可用于改进生存分析中的预测模型。患者隐私需求与预测模型效用可在联邦学习框架下同时得到满足。本文提出了一种联邦生存分析领域的方法，特别针对Cox比例风险模型，重点在于缓解数据异构性并提升模型性能。我们提出一种采用基于特征聚类的联邦学习方法，通过在合成数据集和包括SEER数据库在内的实际应用中提升模型准确性。此外，我们设计了一种基于事件的报告策略，通过响应局部数据变化为模型适应提供动态方法。实验证明了我们方法的有效性，并探讨了联邦学习在医疗领域实际应用的未来方向。