Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices

2025-11-04

AI Summary

To address privacy-preserving electrocardiogram (ECG) classification on resource-constrained heterogeneous IoT devices, this work proposes GAF-fedECG—a lightweight edge-cloud collaborative framework integrating Gramian Angular Field (GAF) encoding and federated learning. Methodologically, raw 1D ECG signals are transformed into 2D GAF images; lightweight CNNs extract features locally, and model aggregation is performed via Federated Averaging (FedAvg). To our knowledge, this is the first empirical deployment of GAF-based federated ECG classification across heterogeneous hardware—including Raspberry Pi 4, laptops, and cloud servers. Experiments under multi-client settings achieve a test accuracy of 95.18%, substantially outperforming single-client baselines, while reducing communication overhead by 37% and memory footprint by 42%. This work demonstrates the feasibility and efficiency of combining GAF representation with federated learning for privacy-sensitive, resource-limited IoT healthcare applications, establishing a novel paradigm for secure edge-intelligent health monitoring.

Technology Category

• Machine Learning: Hardware-aware ML
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Developing privacy-preserving ECG classification using federated learning on IoT devices
• Transforming 1D ECG signals into 2D images for efficient feature extraction
• Validating framework performance across heterogeneous devices including resource-constrained edge devices

Innovation

• Federated learning framework for privacy-preserving ECG classification
• Transforming 1D ECG signals into 2D GAF images
• Validating GAF-based federated learning on heterogeneous IoT devices