Fusion of Personalized Federated Learning (PFL) with Differential Privacy (DP) Learning for Diagnosis of Arrhythmia Disease

• Published in: PloS one Vol. 20; no. 7; p. e0327108
• Main Authors: Bokhari, Syed Mohsin, Sohaib, Sarmad, Shafi, Muhammad
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.01.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Arrhythmia; Arrhythmias, Cardiac - diagnosis; Artificial intelligence; Bradycardia; Cardiac arrhythmia; Classification; Collaboration; Comparative analysis; Correlation coefficient; Correlation coefficients; Customization; Data integrity; Datasets; Deep learning; Disease; EKG; Electrocardiography; Electrocardiography - methods; Federated Learning; Health care; Heart; Humans; Learning; Machine Learning; Mortality; Physiology; Privacy; Real time; Signal processing; Sinuses; Tachycardia; Training; Wavelet transforms

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...