Personalized Federated Learning for ECG Classification Based on Feature Alignment

Jiahui Jin,Renjie Tang,Junzhou Luo,Junbo Qian,Lianbo Ma

doi:10.1155/2021/6217601

Abstract

Electrocardiogram (ECG) data classification is a hot research area for its application in medical information processing. However, insufficient data, privacy preserve, and local deployment are still challenging difficulties. To address these problems, a novel personalized federated learning method for ECG classification is proposed in this paper. First, a global model is trained with federated learning framework on multiple local data clients. Then, we use the global model and private data to train the local model. To reduce the feature inconsistency between global and private local data and for better fitting the private local data, a novel ”feature alignment” module is devised to guarantee the uniformity, which contains two parts, global alignment and local alignment, respectively. For global alignment, the graph metric of batch data is used to constrain the dissimilarity between features generated by the global model and local model. For local alignment, triplet loss is adopted to increase discriminative ability for local private data. Comprehensive experiments on our collected dataset are evaluated. The results show that the proposed method can be better adapted to local data and exhibit superior ability of generalization.

Highlights

Statistics of WHO report that heart disease is the most lethal chronic disease
17.7 million people die of cardiovascular disease every year, which accounts for 31% of the total deaths in the world [1]
It contains many pathological information related to heart activity, and it is an effective way for monitoring and diagnosis of cardiovascular disease [2]

Summary

Introduction

Statistics of WHO report that heart disease is the most lethal chronic disease. Nearly 17.7 million people die of cardiovascular disease every year, which accounts for 31% of the total deaths in the world [1]. In order to expand available cardiovascular information and guarantee the privacy, data from multiple medical institutions can be combined as a unified dataset, which can be used to train a superior global model with federated learning framework [4]. Ere have been works that adopted federated learning for medical data processing and model training [6, 7] In this way, a centralized global model can be trained based on data from a large number of local nodes. When there is large difference between data distributions of global server and local clients, it is hard to directly measure the feature inconsistency between them This makes it difficult to deploy the global ECG classification model to local client with acceptable performance. A global ECG classification model is trained with a typical federated learning method across multiple local clients.

Related Works

Methodology

Experimental Evaluation