Federated Learning for Privacy-Preserving AI: A Secure Approach to Distributed Machine Learning
Abstract
Traditional centralized machine learning approaches often pose privacy risks due to data centralization. Federated learning (FL) offers a decentralized solution, enabling AI models to learn from distributed data without exposing sensitive information. This paper explores FL's potential in privacy-sensitive domains such as healthcare and finance. We evaluate the performance of FL models across multiple edge devices, highlighting improvements in privacy preservation, computational efficiency, and security. Our results indicate that FL can achieve comparable accuracy to centralized learning while maintaining data confidentiality, making it a promising approach for secure AI applications.
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References
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