Federated Learning (FL) is a machine learning framework that enables multiple organizations to train a model without sharing their data with a central server. However, it experiences significant performance degradation if the data is non-identically independently distributed (non-IID). This is a problem in medical settings, where variations in the patient population contribute significantly to distribution differences across hospitals. Personalized FL addresses this issue by accounting for site-specific distribution differences. Clustered FL, a Personalized FL variant, was used to address this problem by clustering patients into groups across hospitals and training separate models on each group. However, privacy concerns remained as a challenge as the clustering process requires exchange of patient-level information. This was previously solved by forming clusters using aggregated data, which led to inaccurate groups and performance degradation. In this study, we propose Privacy-preserving Community-Based Federated machine Learning (PCBFL), a novel Clustered FL framework that can cluster patients using patient-level data while protecting privacy. PCBFL uses Secure Multiparty Computation, a cryptographic technique, to securely calculate patient-level similarity scores across hospitals. We then evaluate PCBFL by training a federated mortality prediction model using 20 sites from the eICU dataset. We compare the performance gain from PCBFL against traditional and existing Clustered FL frameworks. Our results show that PCBFL successfully forms clinically meaningful cohorts of low, medium, and high-risk patients. PCBFL outperforms traditional and existing Clustered FL frameworks with an average AUC improvement of 4.3% and AUPRC improvement of 7.8%.

翻译：联邦学习（FL）是一种使多个机构能够在不与中央服务器共享数据的情况下训练模型的机器学习框架。然而，当数据非独立同分布（non-IID）时，其性能会显著下降。这在医学场景中尤为突出，因患者群体的差异导致不同医院间数据分布存在显著差异。个性化联邦学习通过考虑特定站点的分布差异来解决此问题。作为个性化联邦学习的一种变体，聚类联邦学习通过跨医院将患者聚类成组，并为每组训练独立模型来应对上述挑战。然而，聚类过程需要交换患者级信息，隐私问题依然构成挑战。先前通过使用聚合数据形成聚类来解决此问题，但这会引发分组不准确和性能下降。在本研究中，我们提出隐私保护下基于社区的联邦机器学习（PCBFL），这是一种新颖的聚类联邦学习框架，能够在保护隐私的同时利用患者级数据进行聚类。PCBFL采用安全多方计算（一种密码学技术）来安全地计算跨医院的患者级相似度得分。随后，我们使用eICU数据集中20个站点训练的联邦死亡率预测模型对PCBFL进行评估，并将其性能增益与传统及现有聚类联邦学习框架进行对比。结果表明，PCBFL成功形成了具有临床意义的低风险、中风险和高风险患者群组。与传统及现有聚类联邦学习框架相比，PCBFL平均AUC提升4.3%，AUPRC提升7.8%。