ZK-FedMed: Privacy-Preserving Federated Learning for Cardiovascular and Renal Disease Prediction

Protecting patient data confidentiality while enabling collaborative machine learning across distributed healthcare institutions remains a major challenge. This study presents ZK-FedMed, a privacy-preserving federated learning framework that combines CKKS partially homomorphic encryption for gradient protection, Rényi differential privacy with moments-accountant tracking, zk-SNARK-based integrity verification, TabTransformer feature extraction, SCAFFOLD aggregation, and an approximate federated unlearning procedure. The framework was evaluated on two public benchmark datasets: a cardiovascular disease dataset with 69,997 preprocessed records and a chronic kidney disease dataset expanded through SMOTE from 390 unique records to 4,200 balanced records. ZK-FedMed achieved 91.68% precision, 92.28% recall, and 96.73% AUC for cardiovascular prediction and 88.63% accuracy, 88.41% recall, and 94.52% AUC for renal disease classification. Because both datasets are Kaggle-derived and the CKD cohort is heavily augmented, the results are interpreted as benchmark and simulated-federation evidence rather than proof of real-world multi-hospital clinical efficacy. Additional privacy-budget sensitivity analysis showed that stricter budgets such as ε=1.0 and ε=3.0 substantially reduce utility, while ε=10.0 should be understood as a relaxed operational privacy setting rather than a strong practical privacy guarantee. The findings indicate that explicit cryptographic protection and variance-reduced aggregation can improve privacy-aware federated medical prediction while preserving clinically relevant predictive performance under clearly stated data-source and simulation limitations.