AI, Federated Learning, and Explainable AI for Developing Secure and Robust Healthcare 4.0 Networks
Kapil Shrivastava, Abdulqader Hussein Jumaah, Abual-Hass Adel, Kadim A. Jabbar, Mohit Kumar Sahu · 2025
This study presents a secure federated learning architecture to improve shared model training across multiple nodes in healthcare 4.0 networks while prioritizing data privacy and efficiency. Each node in the recommended technique may define its own starting weights based on local datasets and calculate encrypted model modifications before transmitting them to a central collector. This collector combines these modifications to create global model parameters. Nodes receive this data for repeated improvement. While training, the system protects private data via safe gradient aggregation, convergence checks, and differential privacy. A comparative test demonstrates that the recommended strategy outperforms other AI systems with 94% accuracy, 92% precision, and 90% memory. The approach predicts the future well with an F1 score of 91% and an AUC-ROC of 0.96. Its 90% computational efficiency and 10% connection overhead allow it to manage large datasets. The framework's capacity to expand and protect against threats proves its safety and reliability in healthcare. This innovative method solves the technological criteria for AI usage and fosters confidence among healthcare professionals, allowing them to collaborate and be more creative in healthcare applications.