Real-Time Quantum-Enhanced Federated Learning for Secure, Explainable, and Scalable Chronic Heart Failure Monitoring: A Hybrid Quantum–Classical Framework
Abstract
Real-Time Quantum-Enhanced Federated Learning for Secure, Explainable, and Scalable Chronic Heart Failure Monitoring: A Hybrid Quantum–Classical Framework presents a novel hybrid artificial intelligence framework for intelligent chronic heart failure (CHF) monitoring by integrating quantum-enhanced machine learning, federated learning, blockchain security, explainable artificial intelligence (XAI), and reinforcement learning into a unified privacy-preserving architecture. The proposed QE-FL framework enables collaborative model training across multiple healthcare institutions without sharing sensitive patient data, thereby ensuring data privacy, security, and regulatory compliance while maintaining high diagnostic performance. By combining amplitude-encoded quantum variational circuits for efficient local learning, blockchain-based Byzantine-resilient aggregation for secure model synchronization, a multi-level explainability module for transparent clinical decision-making, and reinforcement learning for personalized treatment optimization, the framework supports real-time, scalable, and trustworthy healthcare applications. Experimental evaluation in a simulated multi-hospital environment demonstrates superior diagnostic accuracy, reduced communication and computational overhead, enhanced robustness against privacy and adversarial attacks, and clinically meaningful improvements in early CHF detection and patient management. These findings establish QE-FL as a promising…
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.