Fair Federated Learning for Multi-Task 6G NWDAF Network Anomaly Detection - Zhang, Chunjiong; Shan, Gaoyang; Roh, Byeong Hee (IEEE Transactions on Intelligent Transportation Systems, 2024)
Future sixth-generation (6G) mobile communication networks are expected to include new features such as the network data analysis function (NWDAF), which will allow network operators to integrate machine learning (ML)-based data analysis techniques into their networks. This will allow NWDAF to identify, safeguard against, and handle various types of anomalous behaviors on user devices. To this end, this study applies fair federated learning (FL) to the 3GPP standard NWDAF architecture and embeds the designed multi-task ML model to detect traffic anomalies in different types of user devices. However, there is a problem of different task demands when the same ML model is used for optimization between different tasks. Therefore, a global alternating gradient projection (AGP) technique is presented in this study. It can be applied to many tasks and utilized to solve minimization problems. The two gradient projection phases comprise each iteration of the AGP. These steps update various tasks at regular intervals, thereby providing a regularized version of the gradient to the original multi-task objective function, which results in optimal task performance. The simulation results demonstrate that the proposed multi-task ML model can simultaneously detect traffic anomalies of different types of user devices in NWDAF and outperforms state-of-the-art models in detecting multi-task anomalies in NWDAF. The experimental evaluation also implied that the designed FL applies superior anomaly detection performance in NWDAF scenarios and has lower communication overhead than that of the traditional NWDAF without affecting the ML performance.
Keywords: Anomaly detection; Fairness; Gradient projections; In networks; Machine learning models; Machine-learning; Multi tasks; Network data; Network data analyze function; User devices; anomaly detection; fairness; Federated learning; multi-task; NWDAF