Federated Learning for Secure AI Model Training Across Distributed Networks
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Abstract
Federated learning (FL) provides a decentralized approach to artificial intelligence (AI) model training, enabling multiple devices or systems to collaboratively train models without sharing raw data. The core challenge lies in maintaining data privacy, security, and the overall efficiency of model convergence across distributed networks. This paper proposes a novel framework for secure federated learning that utilizes advanced encryption techniques, secure aggregation protocols, and differential privacy mechanisms to enhance both privacy and model accuracy. Experimental results from a real-world use case demonstrate the efficiency of this framework, with the proposed model outperforming existing solutions in terms of model convergence speed and privacy protection. The findings suggest that federated learning is a promising paradigm for AI model training in industries that require high data security, such as healthcare and finance.
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