Federated Learning Approaches for Secure and Privacy-Preserving Brain Image Processing
Аннотация
This study introduces federated learning (FL) designs that combine homomorphic encryption, differential privacy, and gradient clipping to secure and speed up brain image processing. Federated learning has revolutionized medical machine learning while respecting privacy. The proposed method lets several medical facilities train deep learning models simultaneously while ensuring data privacy and fulfilling all standards. Federated Averaging (FedAvg) and differential privacy protect data, reduce leaks, and accelerate model convergence. A privacy budget tracker monitors privacy loss and ensures long-term privacy protection. The suggested method outperforms FL techniques in accuracy, communication overhead, computing efficiency, privacy, security, scalability, and energy efficiency. The technology increases accuracy to 94.8%, reduces transmission costs to 150 MB, and increases computing performance to 1.8 million FLOPs per second. The privacy score is 9.5/10, and the security score is 9.0/10, protecting you from dangers. It can scale to 500 clients, making it more scalable than other methods. Health care compliance has increased to 9.8/10, with more people following HIPAA and GDPR. With these advancements, the FL framework is one of the best methods for analyzing brain images with privacy protection. It allows safe, effective, and scalable medical AI app use.