用量子联邦自编码器实现物联网异常检测，保护隐私且高效
Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks
- 结合量子自编码器与联邦学习，边缘设备本地训练不传原始数据
- 在真实物联网数据集上达到与集中式方法相当的检测准确率
- 适合关注隐私保护和高维网络流量分析的研究者
我们提出一种用于物联网异常检测的量子联邦自编码器框架，利用量子联邦学习实现高效、安全、分布式处理。通过量子自编码器进行高维特征表示，结合联邦学习实现去中心化模型训练，使边缘设备上的本地学习无需传输原始数据，从而保护隐私并降低通信开销。该方法利用量子优势提升模式识别能力，显著增强对复杂动态物联网网络流量的检测灵敏度。在真实物联网数据集上的实验表明，所提方法在保持数据隐私的同时，检测准确率和鲁棒性与集中式方法相当。
原文摘要 · Abstract (English)
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.
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