Privacy-Preserving Lightweight Federated Learning for Heterogeneous Data in Internet of Medical Things

The Internet of Medical Things (IoMT) systems enable the continuous monitoring and collection of healthcare data from various medical devices and sensors, facilitating real-time analysis and timely interventions. One such example of the IoMT system is the early and accurate detection of arrhythmia using electrocardiogram (ECG) signals, which plays a crucial role in improving patient health. However, healthcare data contains sensitive information that raises privacy concerns for users. In recent years, Federated Learning (FL) offers a promising solution by enabling collaborative model training on distributed ECG data at the device level while preserving data privacy. However, FL is computationally expensive and suffers from data heterogeneity, which slower convergence, reduces performance, and hinders generalization across diverse client datasets. In this work, we propose a lightweight FL-based model designed explicitly for arrhythmia detection with data heterogeneity. We evaluate our model's performance on two publicly available ECG datasets (PTBD and MIT-BIH arrhythmia). The proposed model achieves high accuracy (between 0.95 and 0.98) while maintaining robustness against heterogeneous data distributions. Furthermore, the experimental results demonstrate significant efficiency gains compared to a baseline model (ResNet). Our proposed lightweight FL-based model requires substantially less mega floating point operations (MFLOPS) (0.07 vs. 2.14 for ResNet) and communication cost (1000 Mb vs. 7500 Mb for ResNet) to achieve convergence. These results indicate the potential of our proposed approach for practical and privacy-preserving arrhythmia detection in resource-constrained IoMT settings.