Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE Annual Congress on Artificial Intelligence of Things (AIoT)

Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series

Kuan-Cheng Chen, Samuel Yen-Chi Chen, Chen-Yu Liu, Kin Kwong Leung

Published Dec 3, 2025• 7 citations
Publisher
DOI
10.1109/aiot66900.2025.00049

Related papers

Federated Variational Quantum Neural Networks for Privacy-Preserving Anomaly Detection in IoT

Radhakrishnan Delhibabu, Manuel Mazzara, Salvatore Distefano

IEEE International Conference on Smart Computing Workshops and Other Affiliated Events (SMARTCOMP Companion)•Jun 1, 2026•0 citations

FedTQK: Communication-Efficient Quantum Kernel Federated Learning for Anomaly Detection in Resource-Constrained IoT Networks

Sungkwan Youm, Kwang-Seong Shin, Jong-Chan Kim

IEEE Internet of Things Journal•Jan 1, 2026•0 citations

Quantum Federated Learning for Privacy-Preserving Anomaly Detection

S.Nazia Banu, A. Sindhu, Farooq Sunar Mahammad +2 more

International Conference on Electronics and Sustainable Communication Systems (ICESC)•Aug 1, 2026•0 citations

Quantum Machine Learning for Anomaly Detection in IoT Devices

Sajid Ahmed Chowdhury, Shaikh Faiyaz Karim, Md. Shahriar Islam Bhuiyan +2 more

International Conference on AI-ML Systems (AIMLSystems)•Oct 8, 2025•1 citation

Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series

Kuan-Cheng Chen, Samuel Yen-Chi Chen, Chen-Yu Liu +1 more

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.

cs.LGCross-listedarXiv:2511.02301•Nov 5, 2025•0 citations

Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel

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.

quant-pharXiv:2603.22366•Mar 24, 2026
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.
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.