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

Oladejo, Adedeji Ojo and Adebayo, Motunrayo and Olufemi, David and Kamau, Eunice and Bobie-Ansah, Deligent and Williams, Daniel (2025) Privacy-Aware AI in cloud-telecom convergence: A federated learning framework for secure data sharing. International Journal of Science and Research Archive, 15 (1). 005-022. ISSN 2582-8185

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.