Privacy preserving AI models for decentralized data management in federated information systems
1 Department of Biosystems and Agricultural Engineering, Ferguson College of Agriculture, Oklahoma State University, USA.
2 Senior Analyst, Modern Retailing at American Airlines.
3 Department of Geography and Planning, Lagos State University, Ojo, Lagos, Nigeria.
4 Department of International Corporate Governance and Financial Regulation, University of Warwick, Coventry, England.
4 Department of International Corporate Governance and Financial Regulation, University of Warwick, Coventry, England.
5 Department of Computer Science, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Ogun State, Nigeria.
Research Article
GSC Advanced Research and Reviews, 2025, 22(02), 104-112.
Article DOI: 10.30574/gscarr.2025.22.2.0043
Publication history:
Received on 02 January 2025; revised on 07 February 2025; accepted on 10 February 2025
Abstract:
Federated information systems represent a transformative approach to decentralized data management and privacy-preserving artificial intelligence. This review critically examines the architectural innovations, technological challenges, and emerging paradigms in federated learning and distributed computing environments. By enabling collaborative model training across disparate data sources without direct data sharing, these systems address critical privacy concerns while maintaining computational efficiency. The research synthesizes current implementation strategies across domains such as healthcare, financial services, and edge computing, highlighting the potential of decentralized machine learning architectures.
Comparative assessments reveal significant advancements in maintaining data confidentiality while extracting meaningful insights. Persistent challenges include communication overhead, model aggregation complexities, and heterogeneous data distribution problems. The investigation explores advanced cryptographic techniques, secure multi-party computation mechanisms, and differential privacy approaches that underpin federated AI models. Emerging research directions emphasize developing robust standardization protocols, enhancing cryptographic safeguards, and creating adaptive federated learning algorithms capable of dynamically responding to evolving privacy and computational requirements.
Keywords:
Federated Learning; Privacy-Preserving AI; Decentralized Data Management; Secure Multi-Party Computation; Machine Learning Privacy; Distributed Computing
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Copyright information:
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
