Electronic medical records (EMRs) supports the development of machine
learning algorithms for predicting disease incidence, patient response to
treatment, and other healthcare events. But insofar most algorithms have been
centralized, taking little account of the decentralized, non-identically
independently distributed (non-IID), and privacy-sensitive characteristics of
EMRs that can complicate data collection, sharing and learning. To address this
challenge, we introduced a community-based federated machine learning (CBFL)
algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustered the
distributed data into clinically meaningful communities that captured similar
diagnoses and geological locations, and learnt one model for each community.
Throughout the learning process, the data was kept local on hospitals, while
locally-computed results were aggregated on a server. Evaluation results show
that CBFL outperformed the baseline FL algorithm in terms of Area Under the
Receiver Operating Characteristic Curve (ROC AUC), Area Under the
Precision-Recall Curve (PR AUC), and communication cost between hospitals and
the server. Furthermore, communities' performance difference could be explained
by how dissimilar one community was to others.

介绍了一种基于社群的联邦机器学习算法，将分布式数据聚类成具有相似诊断和地理位置的有临床意义的社群，使每个社群可以学习一个模型。该算法在现场非 IID ICU EMR 上表现优于传统方法，同时可以保护隐私。

利用分布式电子医疗记录进行联邦机器学习预测死亡率和住院时间的患者聚类提高效率

Patient Clustering Improves Efficiency of Federated Machine Learning to  predict mortality and hospital stay time using distributed Electronic Medical  Records