Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

2026-03-23

AI Summary

This work proposes a distributed privacy-preserving approach for anomaly detection in Internet of Things (IoT) environments by integrating quantum autoencoders with federated learning. Addressing the challenges of privacy leakage and high communication overhead, the method introduces quantum computing into the federated learning framework for the first time, enabling efficient anomaly detection at edge devices without transmitting raw data. By leveraging quantum autoencoders to extract high-dimensional features and incorporating quantum pattern recognition, the model significantly enhances its representational capacity and sensitivity to anomalies in complex network traffic. Experimental results on real-world IoT datasets demonstrate that the proposed approach achieves accuracy and robustness comparable to centralized schemes while substantially reducing communication costs and rigorously preserving data privacy.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• anomaly detection
• IoT networks
• data privacy
• distributed processing
• quantum computing

Innovation

• Quantum Federated Learning
• Quantum Autoencoder
• Anomaly Detection
• IoT Security
• Privacy-Preserving Machine Learning