Federated Learning in Healthcare: Privacy-Preserving AI for Medical Diagnosis
Abstract
Traditional AI models in healthcare often require centralized data aggregation, raising concerns about patient privacy and data security. This research explores the application of federated learning (FL) to train AI models across multiple healthcare institutions without sharing raw patient data. We propose a secure and efficient FL framework that utilizes homomorphic encryption and differential privacy to enhance data protection. Experimental results on medical imaging datasets demonstrate comparable accuracy to centralized models while preserving patient confidentiality, making FL a viable approach for privacy-preserving AI in healthcare.
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