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.

翻译：第五代移动通信（5G）及未来网络日益复杂与异构化，其需满足新兴应用多样化且高标准的要求。电信网络的复杂性与多样性给运维工作带来持续增长的压力。此外，严格的安全与隐私要求为移动运营商利用网络数据提出了挑战。为检测网络故障并预防未来失效，先前研究主要依托传统机器学习/深度学习方法来定位网络异常。现有方法虽功能强大，但未考虑嵌入式且软件密集型的无线接入网络系统间的交织特性。本文提出一种双层联邦图神经网络异常检测与诊断模型，能够在保护隐私的同时检测电信网络异常，并最小化通信开销。该方法的核心是将电信数据概念化为双层时序图神经网络：第一层图捕获网络中不同部署场景下各无线接入网节点间的交互关系，同时每个无线接入网节点进一步被细化为其软件执行图。此外，我们采用联邦学习来解决隐私与安全限制问题。进一步地，本研究基于运营网络真实数据，在三种设定下评估异常检测模型性能：（1）集中式、（2）联邦学习及（3）个性化联邦学习。全面实验表明，个性化联邦时序图神经网络方法在异常检测任务中优于最常用的技术。