Performance comparison with existing models.

This paper presents a novel privacy-preserving architecture, a fusion of Federated Learning with Personalized Models and Differential Privacy (FLPMDP), for diagnosing arrhythmia from 12-lead electrocardiogram (ECG) signals. The architecture supports collaborative training in decentralized healthcare institutions without exposing sensitive patient information. By employing gated recurrent units (GRUs) for temporal sequence modeling along with feature fusion techniques and local differential privacy enforcement, FLPMDP ensures robust classification performance with data confidentiality. The architecture is evaluated on four experimental setups and demonstrates significant performance gain over centralized and federated baseline models. An empirical experiment on a large ECG dataset of 10,646 recordings indicates that the FLPMDP approach achieves an average accuracy of 93.71%. The FLPMDP approach yields F1-scores of 0.98, 0.93, 0.88, and 0.89 for sinus bradycardia (SB), atrial fibrillation (AFIB), supraventricular tachycardia (GSVT), and sinus rhythm (SR), respectively. Additionally, FLPMDP recorded a specificity up to 0.98, with a Kappa score of 0.8971 and a Matthews Correlation Coefficient of 0.9042, indicating high diagnostic accuracy and model strength. Comparative analysis against state-of-the-art methods—such as CNN, ResNet, and attention-based RNNs—indicate that FLPMDP consistently outperforms current models in accuracy, sensitivity, and robustness when facing non-IID data conditions. In the context of this research, federated learning is highly pertinent to modern healthcare, enabling secure and collaborative model training across institutions while complying with data privacy. The proposed FLPMDP framework offers a scalable and privacy-compliant solution for real-time arrhythmia detection, marking a step forward in deploying trustworthy artificial intelligence for decentralized medical diagnostics.