Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks

TL;DR

This paper introduces a novel bi-level federated graph neural network approach for anomaly detection in complex 5G telecom networks, effectively preserving privacy and reducing communication costs.

Contribution

It proposes a bi-level temporal graph neural network model combined with federated learning to detect network faults while maintaining data privacy.

Findings

Personalized federated GNN outperforms traditional methods.

The approach reduces communication costs in federated settings.

Effective anomaly detection in real-world telecom data.

Abstract

5G and Beyond Networks become increasingly complex and heterogeneous, with diversified and high requirements from a wide variety of emerging applications. The complexity and diversity of Telecom networks place an increasing strain on maintenance and operation efforts. Moreover, the strict security and privacy requirements present a challenge for mobile operators to leverage network data. To detect network faults, and mitigate future failures, prior work focused on leveraging traditional ML/DL methods to locate anomalies in networks. The current approaches, although powerful, do not consider the intertwined nature of embedded and software-intensive Radio Access Network systems. In this paper, we propose a Bi-level Federated Graph Neural Network anomaly detection and diagnosis model that is able to detect anomalies in Telecom networks in a privacy-preserving manner, while minimizing communication costs. Our method revolves around conceptualizing Telecom data as a bi-level temporal Graph Neural Networks. The first graph captures the interactions between different RAN nodes that are exposed to different deployment scenarios in the network, while each individual Radio Access Network node is further elaborated into its software (SW) execution graph. Additionally, we use Federated Learning to address privacy and security limitations. Furthermore, we study the performance of anomaly detection model under three settings: (1) Centralized (2) Federated Learning and (3) Personalized Federated Learning using real-world data from an operational network. Our comprehensive experiments showed that Personalized Federated Temporal Graph Neural Networks method outperforms the most commonly used techniques for Anomaly Detection.