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

TL;DR

This paper introduces a federated quantum kernel learning framework that enhances anomaly detection in multivariate IoT time-series by leveraging quantum feature maps for improved accuracy and reduced communication overhead.

Contribution

It presents a novel federated quantum kernel learning approach combining quantum feature maps with federated aggregation for privacy-preserving anomaly detection.

Findings

FQKL outperforms classical federated methods in generalization.

Significant reduction in communication overhead.

Effective detection of complex temporal correlations.

Abstract

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.