Federated Learning for Privacy-Preserving AI: A Decentralized Approach to Data Security
Abstract
With the increasing reliance on AI-driven applications, concerns over data privacy and security have become paramount. Federated learning (FL) offers a decentralized machine learning approach that enables collaborative model training without directly sharing sensitive data. This paper explores the fundamentals of FL, its advantages over traditional centralized AI models, and its application in privacy-sensitive domains such as healthcare, finance, and IoT. We analyze the performance and security trade-offs of FL, discussing challenges such as communication overhead, adversarial attacks, and model poisoning. Our findings suggest that FL provides a promising pathway toward secure and privacy-preserving AI development.
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