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

arXiv: https://arxiv.org/abs/2311.14469 · HTML: https://mlsanity.com/paper/2311.14469/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Federated Learning: Opportunities and Challenges](https://mlsanity.com/paper/2101.05428/) (2021)
- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/2006.11583/) (2020)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Personalized Federated Learning With Graph](https://mlsanity.com/paper/2203.00829/) (2022)
- [Automatic Anomaly Detection in the Cloud Via Statistical Learning](https://mlsanity.com/paper/1704.07706/) (2017)
- [Artificial Intelligence in 3GPP 5G-Advanced: A Survey](https://mlsanity.com/paper/2305.05092/) (2023)

## Built on by (most cited first, 5 of 5)

- [Telecom Foundation Models: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2408.03964/) (2024)
- [Test Code Generation for Telecom Software Systems using Two-Stage Generative Model](https://mlsanity.com/paper/2404.09249/) (2024)
- [Fault Detection in Mobile Networks Using Diffusion Models](https://mlsanity.com/paper/2404.09240/) (2024)
- [Context-Aware Mobile Network Performance Prediction Using Network & Remote Sensing Data](https://mlsanity.com/paper/2405.00220/) (2024)
- [LEED: Local Embedding Evolution Distance for over-smoothing estimation and virtual node selection in GNN](https://mlsanity.com/paper/2608.09596/) (2026)

## Abstract (arXiv)

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.