FEDERATED LEARNING WITH QUANTUM-INSPIRED BOLTZMANN WEIGHTING: ENABLING SECURE AND ROBUST HEART DISEASE PREDICTION

Cardiovascular disease prediction using machine learning faces critical methodological challenges, including data leakage from improper preprocessing sequences, arbitrary feature subset selection without empirical validation, and privacy vulnerabilities in centralized model aggregation. This research proposes a federated quantum-enhanced learning framework that addresses these gaps through: (1) site-specific local preprocessing with global stratified train/test splitting, training-only scaling, and training-only SMOTE application to eliminate data leakage; (2) federated feature selection combining per-site RandomForest importance computation, server-side global aggregation, and multi-k evaluation (k ∈ {5, 7, 9, 11, 13}) with regularized optimization to determine empirically-validated optimal feature subsets (k* = 11); (3) quantum-inspired Boltzmann-weighted secure aggregation (weights ∝ exp(−β·Loss_i)) with convergence monitoring and CTGAN-based generative augmentation for robustness to heterogeneity; and (4) convergence speed metrics tracking accuracy history to identify earliest rounds achieving near-optimal performance, enabling computational efficiency gains. The proposed architecture employs a federated Random Forest classifier (100 trees) trained at each site on CTGAN-augmented data using an empirically optimized 11-feature subset, while maintaining patient data locally at each institution and enabling collaborative model training through encrypted parameter exchange using quantum-safe cryptography. Experimental evaluation on the UCI/Kaggle heart disease dataset demonstrates superior performance compared to a centralized Logistic Regression baseline(99.3% accuracy with AUC 0.9967 for the federated approach vs. 91.8% accuracy for centralized Logistic Regression training), enhanced privacy guarantees through lattice-based LWE-256 quantum-resistant encryption, improved robustness across heterogeneous sites (cross-site performance variance