用联邦学习检测心房颤动，保护隐私还更准。
Feasibility Analysis of Federated Neural Networks for Explainable Detection of Atrial Fibrillation
- 在联邦学习平台上训练神经网络，直接处理原始心电图数据。
- 最佳模型F1达77%，比本地训练平均提升15%。
- 适合关注医疗隐私与可解释性的研究者和开发者。
心房颤动（AFib）因无症状且呈阵发性，早期检测困难。然而，深度学习算法的进步及物联网（IoT）设备收集的海量心电图（ECG）数据为有效解决方案提供了可能。本研究评估了在联邦学习（FL）平台上使用原始ECG数据训练神经网络检测AFib的可行性。在集中式、本地及联邦设置下评估了先进神经网络的性能，研究了不同聚合方法对模型表现的影响，并探索了多种归一化策略以解决神经网络联邦中的问题。结果表明，联邦学习相比本地训练显著提升了检测准确率。最佳联邦模型的F1得分为77%，较各客户端独立训练的平均性能提升15%。本研究强调了联邦学习在医疗诊断中的潜力，为大规模医疗应用提供了一种隐私保护且可解释的解决方案。
原文摘要 · Abstract (English)
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.
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