Comparative Study of Federated Learning and Split Learning for Secure AI Model Training
Abstract
As data privacy regulations become stricter, federated learning (FL) and split learning (SL) have emerged as key techniques for decentralized AI training. This paper presents a comparative study analyzing their effectiveness in privacy-preserving AI applications, including healthcare diagnostics, smart grids, and personalized recommendation systems. We evaluate performance based on communication efficiency, model accuracy, and vulnerability to adversarial attacks. The results offer guidance on selecting the optimal decentralized learning paradigm for secure AI deployments.
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