Securing Federated Learning Enabled NWDAF Architecture With Partial Homomorphic Encryption

Abstract

Network data analytics function (NWDAF), introduced to provision data analytics and machine learning model training in the 5G core network, is expected to be an essential functional entity and play a significant role in the emerging AI-native 6G wireless network. However, refining the NWDAF architecture to support machine learning (ML) model sharing among multiple NWDAFs with distributed data sources and different privacy constraints remains a major challenge. To address this challenge, we propose a federated learning enabled NWDAF architecture with Partial Homomorphic Encryption to secure ML model sharing with privacy preserving. Simulation results demonstrate the feasibility of our proposed architecture.