  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.
