Federated Learning Based Privacy Preserving Cloud Computing Platform for Health Management
Ramgopal Kashyap, Adel Mohamed Mustafa, Maki Mahdi Abdulhasan, Nour Rahim Nimah, Vinay Kumar Dunka
Abstract
Healthcare management has grown increasingly aware of the intersection between shared learning and data security. This research proposes a novel technique to improve models while safeguarding health data. The recommended structure uses cutting-edge federated learning approaches including federated averaging, secure multi-party computing, and differential privacy to increase accuracy and privacy: This technology improves health management systems with regular updates and secure gradient aggregation. Performance tests reveal that the recommended methodology outperforms standard cooperative learning approaches in accuracy, precision, memory, and user pleasure. The technique provides less latency and more flexibility, making it suitable for real-time treatment. The research emphasizes the need for solutions that satisfy moral concerns regarding patient privacy and data security as well as technological issues. This research contributes to the debate about balancing privacy and data analysis as data-driven healthcare solutions become more common. The recommended strategy allows future privacy-protecting and shared learning technologies. Health management environment is safe and effective. The project hopes to gain user and partner trust with these new methods, improving patient outcomes and healthcare data use.