用图像化方法在物联网设备上安全分类心电图，准确率达95.18%
Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices
- 将心电信号转为二维图像，用CNN提取特征并本地训练
- 多设备协同下分类准确率95.18%，比单设备快且更准
- 适合资源受限的医疗物联网场景，兼顾隐私与效率
本研究提出一种用于物联网医疗环境中的隐私保护心电图（ECG）分类联邦学习（FL）框架。通过将一维心电信号转换为二维格拉姆角场（GAF）图像，该方法可借助卷积神经网络（CNN）高效提取特征，同时确保敏感医疗数据始终保留在本地设备。这是首个在异构物联网设备上实证验证基于GAF的联邦心电图分类的工作，量化评估了性能与通信效率。为模拟真实物联网场景，系统部署于服务器、笔记本电脑及资源受限的Raspberry Pi 4上，体现边缘-云融合架构。实验结果表明，该FL-GAF模型在多客户端设置下达到95.18%的高分类准确率，显著优于单客户端基线，在准确率和训练时间上均有提升。尽管增加了GAF转换的计算开销，系统仍保持高效的资源利用与低通信开销。研究验证了轻量级、隐私保护型AI在物联网医疗监测中的潜力，支持智能健康系统中可扩展、安全的边缘部署。
原文摘要 · Abstract (English)
This study presents a federated learning (FL) framework for privacy-preserving electrocardiogram (ECG) classification in Internet of Things (IoT) healthcare environments. By transforming 1D ECG signals into 2D Gramian Angular Field (GAF) images, the proposed approach enables efficient feature extraction through Convolutional Neural Networks (CNNs) while ensuring that sensitive medical data remain local to each device. This work is among the first to experimentally validate GAF-based federated ECG classification across heterogeneous IoT devices, quantifying both performance and communication efficiency. To evaluate feasibility in realistic IoT settings, we deployed the framework across a server, a laptop, and a resource-constrained Raspberry Pi 4, reflecting edge-cloud integration in IoT ecosystems. Experimental results demonstrate that the FL-GAF model achieves a high classification accuracy of 95.18% in a multi-client setup, significantly outperforming a single-client baseline in both accuracy and training time. Despite the added computational complexity of GAF transformations, the framework maintains efficient resource utilization and communication overhead. These findings highlight the potential of lightweight, privacy-preserving AI for IoT-based healthcare monitoring, supporting scalable and secure edge deployments in smart health systems.
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