FedCluster: A Federated Learning Framework for Cross-Device Private ECG Classification

Federated learning technique enables private distributed model to be trained by sharing model parameters instead of raw data among data holders but fails to perform well for private electrocardiogram(ECG) detection. In fact, the ECG data of different clients is extremely non-independent and identically distributed(Non-IID) as a client has only 1 or 2 classes of the total 5 classes of heartbeats. Subset data sharing was proposed to compensate for Non-IID data in image tasks, but the original federated learning algorithm FedAVG with such shared data still performs bad, especially for the rare type, as the parameters from different clients to server are treated equally. Based on our observation that clients can be clustered in terms of their ECG data, we propose a novel parameter update framework, named FedCluster, to improve the federated model to diagnose rare types of clients. To make FedCluster work, we cluster the parameters submitted by different clients to the server to transfer knowledge between similar clients, especially those of rare types, and we propose a hierarchical method for obtaining global shared data suitable for unbalanced ECG data in extreme distribution. Considering the practicality of the proposed method, we only select modified-Lead II of MIT-BIH database to verify our method. The experimental results show that, compared with original FedAVG, 1) our method improves the overall classification accuracy by 6.14%; 2) the recognition accuracy has been improved by 89.38%, using client with high skewness data distribution such as client 232.