Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

• URL: http://arxiv.org/abs/2603.22366v1
• Date: Mon, 23 Mar 2026 05:15:53 GMT
• Title: Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks
• Abstract summary: 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.<n>We show that the proposed method delivers anomaly detection accuracy and robustness comparable to centralized approaches, while ensuring data privacy.
• Score: 7.598284763363093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection [5.822890076771093]
  We present a quantum autoencoder framework that compresses network traffic into latent representations and employs quantum support vector classification for intrusion detection.<n>This work establishes quantum machine learning as a viable, hardware-ready solution for real-world cybersecurity challenges.
  arXiv  Detail & Related papers  (2025-11-26T21:45:02Z)
• A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT [0.4511923587827302]
  We present a lightweight and privacy-preserving botnet detection framework based on federated learning.<n>This approach enables distributed devices to collaboratively train models without exchanging raw data.<n>Experiments on benchmark IoT botnet datasets demonstrate that the framework achieves high detection accuracy while substantially reducing communication costs.
  arXiv  Detail & Related papers  (2025-10-03T20:54:58Z)
• Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey [7.598284763363093]
  This survey explores the integration of Federated Learning (FL) with Network Intrusion Detection Systems (NIDS)<n>FL enables collaborative model training across distributed devices while preserving data privacy.<n>We provide an in-depth investigation of privacy-preserving techniques, model compression approaches, and attack-specific federated solutions for threats including DDoS, MITM, and botnet attacks.
  arXiv  Detail & Related papers  (2025-09-24T01:39:34Z)
• A Quantum Genetic Algorithm-Enhanced Self-Supervised Intrusion Detection System for Wireless Sensor Networks in the Internet of Things [1.049126606580198]
  This paper proposes a novel hybrid Intrusion Detection System that integrates a Quantum Genetic Algorithm (QGA) with Self-Supervised Learning (SSL)<n>The proposed framework is evaluated on benchmark IoT intrusion datasets, demonstrating superior performance in terms of detection accuracy, false positive rate, and computational efficiency.
  arXiv  Detail & Related papers  (2025-09-03T22:02:39Z)
• FedMSE: Semi-supervised federated learning approach for IoT network intrusion detection [0.0]
  The rise of IoT has expanded the cyber attack surface, making traditional centralized machine learning methods insufficient due to concerns about data availability, computational resources, transfer costs, and especially privacy preservation.<n>A semi-supervised federated learning model was developed to overcome these issues, combining the Shrink Autoencoder and Centroid one-class classifier (SAE-CEN)<n>This approach enhances the performance of intrusion detection by effectively representing normal network data and accurately identifying anomalies in the decentralized strategy.
  arXiv  Detail & Related papers  (2024-10-18T02:23:57Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA [78.60275748518589]
  Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks. A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
  arXiv  Detail & Related papers  (2021-01-02T15:21:08Z)
• Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition [101.69873988328808]
  We build upon a quantum convolutional neural network (QCNN) composed of a quantum circuit encoder for feature extraction. An input speech is first up-streamed to a quantum computing server to extract Mel-spectrogram. The corresponding convolutional features are encoded using a quantum circuit algorithm with random parameters. The encoded features are then down-streamed to the local RNN model for the final recognition.
  arXiv  Detail & Related papers  (2020-10-26T03:36:01Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.