The number of IoT devices continues to rise and it is imperative to detect anomalies in real-time to ensure the security, reliability and efficiency of the system. Current deep neural network models have great memory and computing requirements, so it is not easy to use on the edge device. The benefit of federated learning is that it leaves raw data on the individual device, thus improving privacy. Nonetheless, efficient feature learning by compressing the size of quantum datasets has not been built into current federated learning frameworks, and the efficacy of quantum-based approaches under realistic noise in the hardware have not been widely investigated. To address privacy concerns, QEdgeNet is a lightweight hybrid quantum-classical federated learning (QLFL) model for anomaly detection in edge-IoT networks . In this model, the features of network traffic are first compressed by principal component analysis, and then the network traffic is mapped to a compact 4-qubit variational quantum circuit for the classification of anomalous traffic. This model is an aggregated model updated using the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption mechanism, but without exposing the raw network data of clients, and raw traffic data never escapes from a client device during the training process. The applicability of the suggested approach in an idealized simulator is quantified by measuring the inference performance in a simulator simulated quantum hardware noise, the accuracy loss from the hardware noise is recovered by extrapolating to zero noise.
The number of IoT devices continues to rise and it is imperative to detect anomalies in real-time to ensure the security, reliability and efficiency of the system. Current deep neural network models have great memory and computing requirements, so it is not easy to use on the edge device. The benefit of federated learning is that it leaves raw data on the individual device, thus improving privacy. Nonetheless, efficient feature learning by compressing the size of quantum datasets has not been built into current federated learning frameworks, and the efficacy of quantum-based approaches under realistic noise in the hardware have not been widely investigated. To address privacy concerns, QEdgeNet is a lightweight hybrid quantum-classical federated learning (QLFL) model for anomaly detection in edge-IoT networks . In this model, the features of network traffic are first compressed by principal component analysis, and then the network traffic is mapped to a compact 4-qubit variational quantum circuit for the classification of anomalous traffic. This model is an aggregated model updated using the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption mechanism, but without exposing the raw network data of clients, and raw traffic data never escapes from a client device during the training process. The applicability of the suggested approach in an idealized simulator is quantified by measuring the inference performance in a simulator simulated quantum hardware noise, the accuracy loss from the hardware noise is recovered by extrapolating to zero noise.
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.
The growing complexity of Internet of Things (IoT) networks has generated massive multivariate time-series data at the edge, challenging traditional forecasting and anomaly-detection models due to limited computation, memory, and energy on edge devices. Quantum machine learning (QML) provides a promising solution through expressive, parameter-efficient models that align with these constraints. This study proposes two distinct quantum-classical approaches for edge-based IoT analytics. The first combines a Quantum Neural Network (QNN) with a Quantum Autoencoder (QAE) to deliver lightweight forecasting and anomaly detection. The second employs Quantum Long Short-Term Memory (QLSTM) networks, exploring multiple variants and introducing optimization strategies to enable efficient deployment on a Raspberry Pi 5 using PennyLane simulators. Both approaches are evaluated on two real-world IoT datasets Bker (weather parameters) and ETTm1 (transformer temperature) and with a unified preprocessing pipeline incorporating normalization and PCA-based dimensionality reduction. Results show that QLSTM variants achieve superior forecasting accuracy, while QNN+QAE ensures robust anomaly detection with minimal resource overhead. A lightweight QLSTM variant strikes the best balance between accuracy and inference latency, enabling real-time edge deployment. To our knowledge, this is among the first practical demonstrations of QML-based forecasting and anomaly detection directly on edge hardware, highlighting a feasible pathway toward quantum-enhanced IoT intelligence.
The edge computing environment faces a challenge in maintaining data privacy while performing machine learning tasks. The current solutions for privacy preservation are inefficient or do not provide security guarantees, causing a critical gap in the edge computing environment, which is resource-constrained. The aim of this research work is to design a hybrid quantum-classical computing system that addresses the challenges of privacy preservation and efficiency in machine learning tasks. We design a novel architecture combining variational quantum circuits for secure data encoding with classical neural networks for scalable inference, implemented through federated learning protocols enhanced with quantum differential privacy mechanisms. Experimental evaluation demonstrates a 35% reduction in communication overhead compared to traditional federated learning approaches while maintaining 99.2% classification accuracy across diverse datasets. The quantum-enhanced privacy mechanisms achieve provable security guarantees with information-theoretic bounds, outperforming conventional cryptographic methods by 28% in computational overhead reduction. This hybrid approach establishes a new paradigm for secure and efficient machine learning deployment on edge devices, enabling privacy-preserving artificial intelligence in healthcare, finance, and IoT applications.
The rapid growth of industrial Internet of Things (IIoT) systems has created new challenges for anomaly detection in high-dimensional, multivariate time-series, where privacy, scalability, and communication efficiency are critical. Classical federated learning approaches mitigate privacy concerns by enabling decentralized training, but they often struggle with highly non-linear decision boundaries and imbalanced anomaly distributions. To address this gap, we propose a Federated Quantum Kernel Learning (FQKL) framework that integrates quantum feature maps with federated aggregation to enable distributed, privacy-preserving anomaly detection across heterogeneous IoT networks. In our design, quantum edge nodes locally compute compressed kernel statistics using parameterized quantum circuits and share only these summaries with a central server, which constructs a global Gram matrix and trains a decision function (e.g., Fed-QSVM). Experimental results on synthetic IIoT benchmarks demonstrate that FQKL achieves superior generalization in capturing complex temporal correlations compared to classical federated baselines, while significantly reducing communication overhead. This work highlights the promise of quantum kernels in federated settings, advancing the path toward scalable, robust, and quantum-enhanced intelligence for next-generation IoT infrastructures.
The rapid expansion of the Internet of Things in critical industrial environments has significantly increased the attack surface, exposing systems to sophisticated cyber threats. Traditional pattern-based intrusion detection systems struggle to detect such advanced attacks, while deep learning approaches, despite achieving high detection accuracy, often suffer from high computational cost and latency, limiting their deployment in resource-constrained edge gateways. Quantum machine learning offers a promising alternative by enabling high-dimensional feature representation; however, current implementations are constrained by hardware noise, limited qubit availability and backend-dependent execution characteristics in the Noisy Intermediate-Scale Quantum era. To address these challenges, this paper proposes a Residual Hybrid Quantum-Classical Neural Network (RHQ-CNN) for efficient intrusion detection in Edge-Industrial Internet of Things (Edge-IIoT) environments under idealized quantum simulation conditions. The proposed framework employs a classical neural network encoder to compress high-dimensional network traffic into a compact latent representation suitable for quantum processing. A variational quantum circuit with serial data re-uploading is then utilised to model complex non-linear decision boundaries without increasing qubit requirements. In addition, a residual connection fuses classical and quantum representations to improve training stability and preserve latent feature information. The model is evaluated on the Edge-IIoTset dataset and achieves a test accuracy of 99.94%, with high weighted performance across the 15-class detection task, although the extremely low-sample Fingerprinting class remains comparatively more challenging. Additional controlled ablation, deployment-cost, and bootstrap confidence interval analyses demonstrate the test-set metric stability and computational trade-offs of the proposed architecture. These findings highlight the potential of hybrid quantum-classical models for next-generation cybersecurity in industrial IoT systems.
Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context and anomaly-labeled data are limited. Quantum federated learning (QFL) overcomes these concerns by distributing model training among several quantum clients, consequently eliminating the requirement for centralized quantum storage and processing. However, in real-life quantum networks, clients frequently differ in terms of hardware capabilities, circuit designs, noise levels, and how classical data is encoded or preprocessed into quantum states. These differences create inherent heterogeneity across clients - not just in their data distributions, but also in their quantum processing behaviors. As a result, training a single global model becomes ineffective, especially when clients handle imbalanced or non-identically distributed (non-IID) data. To address this, we propose a new framework called personalized quantum federated learning (PQFL) for anomaly detection. PQFL enhances local model training at quantum clients using parameterized quantum circuits and classical optimizers, while introducing a quantum-centric personalization strategy that adapts each client's model to its own hardware characteristics and data representation. Extensive experiments show that PQFL significantly improves anomaly detection accuracy under diverse and realistic conditions. Compared to state-of-the-art methods, PQFL reduces false errors by up to 23%, and achieves gains of 24.2% in AUROC and 20.5% in AUPR, highlighting its effectiveness and scalability in practical quantum federated settings.