A Comparative Study of Federated Learning and Centralized AI Models for Privacy-Preserving Data Analysis
Abstract
With increasing concerns over data privacy, federated learning (FL) has emerged as a promising alternative to traditional centralized AI models. This paper presents a comparative study between FL and centralized learning methods, focusing on model accuracy, computational efficiency, and privacy protection. We evaluate their performance across healthcare, finance, and IoT applications, demonstrating the trade-offs in communication overhead, data security, and model generalization. The results provide a roadmap for implementing FL in privacy-sensitive AI deployments.
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