Blockchain-Governed Federated Learning with Sparse-Causal Bi- RNN Transformer Net for Secure and Intelligent Healthcare Analytics

To address the challenges of data privacy, temporal modeling inefficiencies, and lack of trust in centralized healthcare AI, this research proposes a novel Blockchain-Governed Federated Deep Learning Framework (B-FDLF). This framework integrates a hybrid architecture named Sparse-Causal Bi-RNN Transformer Net, designed for secure, scalable, and accurate prediction of neurodegenerative diseases like Parkinson’s. The model combines CNNs for local spatio-temporal feature extraction, Bidirectional RNNs for sequential learning, and a Sparse-Causal Transformer for long-range temporal dependency modeling with causal masking. To preserve patient privacy, federated learning enables local training at healthcare edge nodes, while blockchain integration ensures tamper-proof, auditable, and verifiable model updates via smart contracts. Experimental validation on the UCI Parkinson’s biomedical voice dataset demonstrates superior performance of the proposed method, achieving 94.8% accuracy, 93.9% precision, 94.3% recall, and 94.1% F1-score, while minimizing communication overhead (48.3 MB), privacy leakage (1.2%), and achieving high adversarial detection rate (96.4%). These results confirm that B-FDLF is a robust and privacy-preserving AI framework well-suited for real-world, decentralized healthcare applications.