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Secured Medical Recommendations via Federated Learning by tanishqshahare2003 is a document available to read on EtoBox.

This paper presents a federated learning approach for a medical recommendation system that utilizes homomorphic encryption to enhance user privacy by preventing gradient leakage during model training. The proposed method shows improved privacy preservation with minimal impact on accuracy, allowing computations on encrypted gradients without exposing sensitive user data. The research highlights the importance of maintaining user confidentiality in medical data management while leveraging distributed learning

Author

tanishqshahare2003

Language

EN