A Safe Architecture of 5G Network Intelligence Based on Federated Learning and NWDAF

Abstract

Abstract: Intelligent communication network is the technical development trend of 5G and post-5G era. Big data analysis is the foundation of intelligent network while data isolation and privacy protection in network data analysis is a bottleneck problem. Federated learning is an emerging distributed machine learning framework which can make use of all parties' data for joint modeling while protecting the data privacy. In this paper, we propose an intelligent communication network framework which combines 5G Network Data Analysis Function (NWDAF) and federated learning to solve the above problem. Our work demonstrates that federated learning technology can ensure the data usage compliance in the process of 5G network intellectualization while solving the data isolation and data privacy protection problem.