Privacy-Preserving Federated Learning Models for Secure Distributed Data Intelligence
Abstract
Privacy-preserving federated learning models enable secure distributed data intelligence by allowing organizations to collaboratively train machine learning models without directly sharing sensitive data. The proposed framework combines federated optimization, encryption, differential privacy, and secure aggregation to protect information throughout the learning process. It supports decentralized analytics across heterogeneous enterprise environments while maintaining data confidentiality, model accuracy, and regulatory compliance. By reducing centralized data exposure, the framework improves collaborative intelligence, strengthens security, and enables scalable privacy-aware decision-making across distributed systems.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 10 No. 2 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
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Published |
March 6, 2025 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Jean-Baptiste Kempf (%2025). Privacy-Preserving Federated Learning Models for Secure Distributed Data Intelligence. International Journal of Emerging Trends in Engineering and Management Research , Vol. 10 No. 2 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) . https://doi.org/10.15662/qpzmrw57 |
References
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