Privacy-Aware AI in cloud-telecom convergence: A federated learning framework for secure data sharing
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.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.