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

arXiv: https://arxiv.org/abs/2511.02301 · HTML: https://mlsanity.com/paper/2511.02301/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 54 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 29 of 29)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Federated Learning with Partial Model Personalization](https://mlsanity.com/paper/2204.03809/) (2022)
- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach](https://mlsanity.com/paper/2007.09712/) (2020)
- [Quantum Federated Learning with Quantum Data](https://mlsanity.com/paper/2106.00005/) (2021)
- [Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks](https://mlsanity.com/paper/1710.09207/) (2017)
- [Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing](https://mlsanity.com/paper/2007.07122/) (2020)
- [A Survey on Anomaly Detection for Technical Systems using LSTM Networks](https://mlsanity.com/paper/2105.13810/) (2021)
- [Slimmable Quantum Federated Learning](https://mlsanity.com/paper/2207.10221/) (2022)
- [Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting](https://mlsanity.com/paper/2412.08851/) (2024)
- [Validating Large-Scale Quantum Machine Learning: Efficient Simulation of Quantum Support Vector Machines Using Tensor Networks](https://mlsanity.com/paper/2405.02630/) (2024)
- [CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data](https://mlsanity.com/paper/2409.08584/) (2024)
- [Quantum Kernel-Based Long Short-term Memory](https://mlsanity.com/paper/2411.13225/) (2024)
- [Consensus-based Distributed Quantum Kernel Learning for Speech Recognition](https://mlsanity.com/paper/2409.05770/) (2024)
- [Robust Decentralized Quantum Kernel Learning for Noisy and Adversarial Environment](https://mlsanity.com/paper/2504.13782/) (2025)
- [Coupled Attention Networks for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2306.07114/) (2023)
- [Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series](https://mlsanity.com/paper/2208.01998/) (2022)
- [Resource Sharing in the Edge: A Distributed Bargaining-Theoretic Approach](https://mlsanity.com/paper/2001.04229/) (2020)
- [Quantum Machine Learning for UAV Swarm Intrusion Detection](https://mlsanity.com/paper/2509.01812/) (2025)

## Built on by (most cited first, 1 of 1)

- [Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors](https://mlsanity.com/paper/2603.12540/) (2026)

## Abstract (arXiv)

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.