Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

TL;DR

This paper introduces a Quantum Federated Autoencoder framework that combines quantum autoencoders and federated learning to improve anomaly detection in IoT networks, emphasizing privacy and efficiency.

Contribution

It presents a novel integration of quantum autoencoders with federated learning for decentralized, privacy-preserving anomaly detection in IoT environments.

Findings

Achieves anomaly detection accuracy comparable to centralized methods

Enhances detection sensitivity using quantum advantage

Ensures data privacy and reduces communication overhead

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.