A Privacy Protecting Structure for Federated Learning in Intelligent Health Systems
Ramgopal Kashyap, Ali Hamid AbdulHussein, Ahmed Read Al-Tameemi, Abual-Hass Adel, Amit Bhagat · 2025
This study describes a privacy-protected shared learning mechanism for smart health systems. This arrangement provides secure data exchange and privacy. Many schools can train machine learning models simultaneously using federated learning without sharing patient data. Advanced methods, including differential privacy, secure multi-party computing, and homomorphic encryption, protect data in the recommended method. Each client’s data attributes determine model changes using adaptive learning. Performance metrics, including memory, accuracy, and precision, improve. The system also incorporates safe aggregation and private stochastic gradient descent. These features protect personal data and reduce interaction. The suggested technique improves security using modern cryptographic technologies like trusted execution environments and secure enclave computing. The technique outperforms existing privacy-protecting systems in data privacy, scalability, and computing efficiency. The recommended structure follows rigorous data privacy regulations and improves healthcare app performance. Our technology provides a solid and scalable foundation for medical system shared learning. Patient data is protected, and real-time healthcare choices are accurate and dependable.