Federated Learning: A Privacy-Preserving Approach to Decentralized AI
Abstract
With increasing concerns over data privacy and security, federated learning (FL) has emerged as a promising solution for training AI models without centralizing sensitive data. This paper explores the architecture, benefits, and challenges of FL in applications such as healthcare, finance, and smart devices. We propose an enhanced FL framework integrating differential privacy and secure multi-party computation to improve security while maintaining model performance. Experimental results on benchmark datasets demonstrate improved privacy preservation with minimal accuracy trade-offs. The study also discusses challenges related to communication overhead, adversarial attacks, and real-world deployment.
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