A Novel Federated Learning Framework for Privacy-Preserving AI Applications
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Abstract
As AI applications expand, concerns about data privacy and security grow. This paper introduces a novel federated learning framework that enables collaborative AI model training without sharing raw data. The framework incorporates advanced encryption techniques and differential privacy to ensure secure data exchanges between devices. Experiments on healthcare and financial datasets demonstrate the framework’s ability to achieve high model accuracy while preserving user privacy. The proposed approach addresses key challenges in decentralized AI, fostering trust and enabling broader adoption of privacy-preserving technologies.
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References
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