Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis. Federated learning (FL) has attracted growing attention as a promising framework for collaborative model development, as it allows multiple institutions to jointly train predictive models without directly sharing or centralizing raw data. Nevertheless, its practical performance, robustness, and privacy-preserving benefits remain insufficiently evaluated using real-world clinical datasets. To bridge this gap, this study systematically examines the application of federated learning to multi-center sepsis prediction. The experimental dataset consists of 648 clinically screened samples collected from three tertiary hospitals in China, with rigorous inclusion and exclusion criteria. We establish a centralized training paradigm as the performance baseline, and then implement a horizontal federated learning framework for distributed collaborative modeling. Extensive experimental results demonstrate that the federated learning-based model achieves highly comparable prediction accuracy to the centralized counterpart, while fundamentally avoiding privacy leakage. Further privacy security analysis verifies that malicious attackers cannot reconstruct the original patient data from the transmitted model parameters, indicating strong resistance against data reconstruction attacks. This work not only validates the practicality and security of federated learning in clinical sepsis prediction, but also provides a reliable and feasible solution for privacy-preserving multi-center medical collaboration.

翻译：多中心医疗数据的隐私敏感性和分布式特性，给准确早期预测脓毒症的集中式建模带来了严重障碍。联邦学习作为一种有前景的协作模型开发框架，因其允许多个机构在不直接共享或集中原始数据的情况下联合训练预测模型而日益受到关注。然而，其实际性能、鲁棒性和隐私保护优势尚未利用真实临床数据集得到充分评估。为弥补这一不足，本研究系统性地考察了联邦学习在多中心脓毒症预测中的应用。实验数据集包含从中国三家三甲医院收集的648份经过临床筛选的样本，并采用了严格的纳入和排除标准。我们建立集中式训练范式作为性能基准，然后实现了一个横向联邦学习框架用于分布式协作建模。大量实验结果表明，基于联邦学习的模型实现了与集中式模型高度可比的预测准确性，同时根本上避免了隐私泄露。进一步的隐私安全分析验证了恶意攻击者无法从传输的模型参数中重建患者的原始数据，表明该方法对数据重建攻击具有强抵抗能力。这项工作不仅验证了联邦学习在临床脓毒症预测中的实用性和安全性，也为隐私保护的多中心医疗协作提供了可靠且可行的解决方案。