# Clustered Federated Learning Architecture for Network Anomaly Detection in Large Scale Heterogeneous IoT Networks

arXiv: https://arxiv.org/abs/2303.15986 · HTML: https://mlsanity.com/paper/2303.15986/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2023.

## Built on by (most cited first, 7 of 7)

- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding](https://mlsanity.com/paper/2410.09284/) (2024)
- [A survey on Clustered Federated Learning: Taxonomy, Analysis and Applications](https://mlsanity.com/paper/2501.17512/) (2025)
- [TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clustering](https://mlsanity.com/paper/2409.10392/) (2024)
- [MI2DAS: A Multi-Layer Intrusion Detection Framework with Incremental Learning for Securing Industrial IoT Networks](https://mlsanity.com/paper/2602.23846/) (2026)
- [An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks](https://mlsanity.com/paper/2602.24209/) (2026)
- [CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification](https://mlsanity.com/paper/2605.06571/) (2026)

## Abstract (arXiv)

There is a growing trend of cyberattacks against Internet of Things (IoT) devices; moreover, the sophistication and motivation of those attacks is increasing. The vast scale of IoT, diverse hardware and software, and being typically placed in uncontrolled environments make traditional IT security mechanisms such as signature-based intrusion detection and prevention systems challenging to integrate. They also struggle to cope with the rapidly evolving IoT threat landscape due to long delays between the analysis and publication of the detection rules. Machine learning methods have shown faster response to emerging threats; however, model training architectures like cloud or edge computing face multiple drawbacks in IoT settings, including network overhead and data isolation arising from the large scale and heterogeneity that characterizes these networks. This work presents an architecture for training unsupervised models for network intrusion detection in large, distributed IoT and Industrial IoT (IIoT) deployments. We leverage Federated Learning (FL) to collaboratively train between peers and reduce isolation and network overhead problems. We build upon it to include an unsupervised device clustering algorithm fully integrated into the FL pipeline to address the heterogeneity issues that arise in FL settings. The architecture is implemented and evaluated using a testbed that includes various emulated IoT/IIoT devices and attackers interacting in a complex network topology comprising 100 emulated devices, 30 switches and 10 routers. The anomaly detection models are evaluated on real attacks performed by the testbed's threat actors, including the entire Mirai malware lifecycle, an additional botnet based on the Merlin command and control server and other red-teaming tools performing scanning activities and multiple attacks targeting the emulated devices.