Feasibility Analysis of Federated Neural Networks for Explainable Detection of Atrial Fibrillation

TL;DR

This paper explores the use of federated learning with neural networks to improve the detection of atrial fibrillation from ECG data, emphasizing privacy and scalability.

Contribution

It demonstrates the feasibility and effectiveness of federated neural networks for AFib detection, outperforming local models and addressing federation challenges.

Findings

Federated learning improves AFib detection accuracy by 15% over local models.

The best federated model achieved an F1 score of 77%.

Different aggregation and normalization strategies impact model performance.

Abstract

Early detection of atrial fibrillation (AFib) is challenging due to its asymptomatic and paroxysmal nature. However, advances in deep learning algorithms and the vast collection of electrocardiogram (ECG) data from devices such as the Internet of Things (IoT) hold great potential for the development of an effective solution. This study assesses the feasibility of training a neural network on a Federated Learning (FL) platform to detect AFib using raw ECG data. The performance of an advanced neural network is evaluated in centralized, local, and federated settings. The effects of different aggregation methods on model performance are investigated, and various normalization strategies are explored to address issues related to neural network federation. The results demonstrate that federated learning can significantly improve the accuracy of detection over local training. The best performing federated model achieved an F1 score of 77\%, improving performance by 15\% compared to the average performance of individually trained clients. This study emphasizes the promise of FL in medical diagnostics, offering a privacy-preserving and interpretable solution for large-scale healthcare applications.