A Federated Learning-Enabled Privacy-Preserving AI Framework for Secure and Distributed Data Processing

Abstract

In this paper, a novel federated learning paradigm for privacy-preserving artificial intelligence (AI) in distributed settings is presented. The proposed framework has the potential to overcome the inherent limitations of conventional centralized machine learning systems, such as data privacy concerns, security vulnerabilities, and regulatory compliance challenges in centralized data collection. The proposed architecture is such that multiple distributed clients are training a common AI model, while locally keeping sensitive data. The clients do not send the raw data to the central aggregation server, but only the secure model parameters. This substantially reduces the risk of leakage of data. To further enhance privacy and security, the framework introduces the concept of secure aggregation and differential privacy in the federated training process, thus safeguarding personal client data during the process of updating the model. The proposed approach is compared with conventional centralized trained learning and conventional federated learning model, based on various performance parameters such as classification accuracy, convergence rate, communication efficiency, privacy preservation, and data exposure resistance. Experimental results show that the improved framework can achieve competitive predictive accuracy, converge quickly, realize efficient communication and resist privacy attack while ensuring the model performance is good. The proposed framework provides a scalable and secure solution that protects sensitive information while simultaneously prioritizing privacy in distributed AI applications, particularly in industries like healthcare, finance, smart manufacturing, and the Internet of Things (IoT) sector. The research findings suggest that improved federated learning can achieve an optimal predictive performance, privacy protection, and secure collaborative learning, which makes it a viable method for next-generation distributed AI systems.