Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

Devashish Chaudhary; Sutharshan Rajasegarar; Shiva Raj Pokhrel

quant-phMar 23, 2026

Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

Authors: Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel

Organizations: School of Information Technology, Deakin University, Geelong, Australia

Abstract

We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for efficient, secure, and distributed processing in IoT networks. By harnessing quantum autoencoders for high-dimensional feature representation and federated learning for decentralized model training, the approach transforms localized learning on edge devices without requiring transmission of raw data, thereby preserving privacy and minimizing communication overhead. The model leverages quantum advantage in pattern recognition to enhance detection sensitivity, particularly in complex and dynamic IoT network traffic. Experiments on a real-world IoT dataset show that the proposed method delivers anomaly detection accuracy and robustness comparable to centralized approaches, while ensuring data privacy.

Figures & tables

Fig. 1 : Quantum Federated Autoencoder Framework for Anomaly Detection.

Fig. 2 : Quantum Autoencoder with 4 qubits: 3 latent, 1 trash.

Scenario I					Scenario II			Scenario III
Normal	E1 $\rightarrow$ R1	E2 $\rightarrow$ R1	E3 $\rightarrow$ R3	E4 $\rightarrow$ R2	R1 $\rightarrow$ C	R2 $\rightarrow$ C	R3 $\rightarrow$ R2	E1 $\rightarrow$ R1	E2 $\rightarrow$ R1	E3 $\rightarrow$ R3	E4 $\rightarrow$ R2	R1 $\rightarrow$ C	R2 $\rightarrow$ C	R3 $\rightarrow$ C
Attack	E1 $\rightarrow$ R2	E2 $\rightarrow$ R2	E3 $\rightarrow$ R1	E4 $\rightarrow$ R1	R1 $\rightarrow$ R2	R2 $\rightarrow$ R1	R3 $\rightarrow$ R1	E1 $\rightarrow$ A	E2 $\rightarrow$ A	E3 $\rightarrow$ A	E4 $\rightarrow$ A	R1 $\rightarrow$ A	R2 $\rightarrow$ A	R3 $\rightarrow$ A
	E1 $\rightarrow$ R3	E2 $\rightarrow$ R3	E3 $\rightarrow$ R2	E4 $\rightarrow$ R3	R1 $\rightarrow$ R3		R3 $\rightarrow$ C
	E1 $\rightarrow$ C	E2 $\rightarrow$ C	E3 $\rightarrow$ C	E4 $\rightarrow$ C

TABLE I : Attack scenarios: C – Coordinator, A – Attacker, Rx – Router, Ex – Edge/Leaf device.

Fig. 3 : Network topology with Coordinator (C), Routers (R), Edge nodes (E) and Attack node (A).

Routers	Method	Accuracy	Precision	Recall	F1
R1	Hierarchical FL	0.91	0.94	0.91	0.92
	FedAvg FL	0.93	0.94	0.93	0.93
	Centralized	0.88	0.93	0.88	0.90
R2	Hierarchical FL	0.91	0.91	0.91	0.91
	FedAvg FL	0.91	0.91	0.91	0.91
	Centralized	0.91	0.91	0.91	0.91

TABLE II : Performance metrics across routers for FL methods

Panel of Figure 6 — Fig. 4 : Comparison of FL methods for 3 devices over 5 rounds: (a) Hierarchical FL and (b) Standard FedAvg FL.

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Mar 23, 2026J/K move · Enter open · S save

In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis

Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel, Lei Pan+1

School of IT, Deakin University, Geelong, Australia · School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

IoT Intrusion Detection Network Intrusion Detection Reconstruction-Based Anomaly Detection Federated Learning

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The rapid expansion of the Internet of Things (IoT) and its integration with backbone networks have heightened the risk of security breaches. Traditional centralized approaches to anomaly detection, which require transferring large volumes of data to central servers, suffer from privacy, scalability, and latency limitations. This paper proposes a lightweight autoencoder-based anomaly detection framework designed for deployment on resource-constrained edge devices, enabling real-time detection while minimizing data transfer and preserving privacy. Federated learning is employed to train models collaboratively across distributed devices, where local training occurs on edge nodes and only model weights are aggregated at a central server. A real-world IoT testbed using Raspberry Pi sensor nodes was developed to collect normal and attack traffic data. The proposed federated anomaly detection system, implemented and evaluated on the testbed, demonstrates its effectiveness in accurately identifying network attacks. The communication overhead was reduced significantly while achieving comparable performance to the centralized method.

Abstract

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