Performance comparison with existing models.

<div><p>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 dece...

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Bibliografiska uppgifter
Huvudupphovsman:	Syed Mohsin Bokhari (21702906) (author)
Övriga upphovsmän:	Sarmad Sohaib (21273402) (author), Muhammad Shafi (7168253) (author)
Publicerad:	2025
Ämnen:	Cell Biology Neuroscience Biotechnology Mental Health Biological Sciences not elsewhere classified Information Systems not elsewhere classified matthews correlation coefficient four experimental setups 646 recordings indicates time arrhythmia detection sr ), respectively iid data conditions flpmdp approach achieves federated baseline models large ecg dataset feature fusion techniques personalized federated learning personalized models flpmdp ), federated learning diagnosing arrhythmia gsvt ), flpmdp recorded data confidentiality data privacy xlink "> step forward sinus rhythm sinus bradycardia paper presents modern healthcare model strength lead electrocardiogram kappa score highly pertinent facing non enabling secure empirical experiment compliant solution comparative analysis 71 %.
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Performance comparison with existing models.

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