The increased availability of medical data has significantly impacted healthcare by enabling the application of machine / deep learning approaches in various instances. However, medical datasets are usually small and scattered across multiple providers, suffer from high class-imbalance, and are subject to stringent data privacy constraints. In this paper, the application of a data regularization algorithm, suitable for learning under high class-imbalance, in a federated learning setting is proposed. Specifically, the goal of the proposed method is to enhance model performance for cardiovascular disease prediction by tackling the class-imbalance that typically characterizes datasets used for this purpose, as well as by leveraging patient data available in different nodes of a federated ecosystem without compromising their privacy and enabling more resource sensitive allocation. The method is evaluated across four datasets for cardiovascular disease prediction, which are scattered across different clients, achieving improved performance. Meanwhile, its robustness under various hyperparameter settings, as well as its ability to adapt to different resource allocation scenarios, is verified.

提出了一种适用于高类别不平衡情况下学习的数据正则化算法在联邦学习环境中的应用，以提高心血管疾病预测模型的性能，并利用分布在联邦生态系统不同节点上的患者数据，不损害其隐私并实现更加资源敏感的分配。该方法在分散在不同客户端的四个心血管疾病预测数据集上进行评估，取得了改进的性能，并验证了在不同超参数设置和资源分配场景下的稳健性和适应能力。

在联邦学习环境中通过数据规范化提高高度不平衡的医学数据性能