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Privacy-Aware AI in cloud-telecom convergence: A federated learning framework for secure data sharing

Year: 2025

Type: article

Abstract: With the increasing demand for integrated cloud and telecommunications (cloud-telecom convergence), the need for privacy-preserving artificial intelligence (AI) models has never been more urgent. Federated learning (FL) has emerged as a powerful framework that facilitates secure and privacy-aware machine learning models, without the need to share raw data between entities. This paper explores the role of federated learning in ensuring secure data sharing within cloud-telecom convergence, with a focus on privacy preservation. We discuss the fundamental concepts of privacy-aware AI, cloud-telecom integration, and federated learning. Moreover, we highlight the challenges, key research directions, and practical implementations of these technologies to achieve secure and scalable data sharing in 5G/6G environments. Through a systematic review of recent advances and future trends, we demonstrate the promise of federated learning in enabling privacy-preserving AI solutions in this domain.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17098389

Authors: Oladejo, Adedeji Ojo, Adebayo, Motunrayo, Olufemi, David, Kamau, Eunice, Bobie-Ansah, Deligent, Williams, Daniel

Institutions: Ohio University, Indiana Wesleyan University, University of Fairfax, Maharishi International University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7081183289

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