Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI

Privacy-preserving AI in healthcare is achievable: The paper demonstrates that federated learning allows hospitals and clinics to train AI models for heart rhythm detection without sharing sensitive patient data across institutions, addressing a major barrier to AI adoption in healthcare settings.

AI transparency matters for clinical trust: The framework includes explainable AI (XAI) components that help doctors understand why the system flagged a particular heart rhythm as abnormal, rather than treating the AI as a "black box"—a critical requirement for clinicians to make reliable medical decisions.

High accuracy is possible with limited data: By combining federated learning with transfer learning techniques, the system achieved 94-98% accuracy in detecting arrhythmias even when trained on distributed datasets rather than requiring all data in one location, making the approach practical for real-world healthcare networks.

Data quality significantly impacts performance: The 4% difference in accuracy between noisy and clean data (94% vs. 98%) highlights that healthcare AI systems require careful attention to data quality and preprocessing, not just model sophistication.

Responsible AI design requires multiple safeguards: This work illustrates that addressing real-world healthcare challenges requires combining three elements—privacy protection (federated learning), model transparency (explainable AI), and clinical validation—rather than focusing on accuracy alone.