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Federated Learning Based Privacy Preserving Data Analytics Framework for Intelligent Cloud Applications

J. Hariharan

Mohamed Sathak Engineering College, Kilakkarai, Tamil Nadu, India

Abstract

The increasing adoption of intelligent cloud applications in domains such as healthcare, finance, smart cities, and industrial IoT has led to massive generation of distributed data across multiple devices and organizations. While cloud-based data analytics enables powerful insights and decision-making capabilities, it also raises critical concerns related to data privacy, security, and regulatory compliance. Traditional centralized machine learning approaches require raw data to be collected and stored in a central server, increasing the risk of data breaches, privacy violations, and unauthorized access. To address these challenges, this research proposes a Federated Learning (FL)-based privacy-preserving data analytics framework for intelligent cloud applications. The proposed framework enables collaborative machine learning across multiple distributed nodes without transferring raw data to a central server. Instead, only model updates are shared, ensuring strong data privacy protection. The framework integrates secure aggregation techniques, differential privacy mechanisms, and encrypted communication protocols to enhance security during distributed model training. Experimental evaluation is conducted using simulated cloud environments with multiple distributed clients and a central aggregation server. Performance metrics such as model accuracy, communication overhead, training latency, and privacy preservation level are analyzed. The results demonstrate that the proposed FL-based framework effectively maintains high analytical accuracy while ensuring strong privacy protection and reducing data leakage risks. The study contributes to the development of secure, scalable, and privacy-preserving intelligent cloud data analytics systems.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 4 No. 1 (2019): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2019.0401001

Pages

4775-4783

Published

January 9, 2019
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

J. Hariharan (%2019). Federated Learning Based Privacy Preserving Data Analytics Framework for Intelligent Cloud Applications. International Journal of Emerging Trends in Engineering and Management Research , Vol. 4 No. 1 (2019): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 4775-4783. https://doi.org/10.15662/ijetemr.2019.0401001

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