← Back ◌ Quantum Computing Mar 25, 2026

Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

arXiv Quantum Archived Mar 25, 2026 ✓ Full text saved

arXiv:2603.22366v1 Announce Type: new 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

Full text archived locally

✦ AI Summary · Claude Sonnet

Quantum Physics [Submitted on 23 Mar 2026] Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel 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. Comments: This paper has been accepted at ICOIN 2026 Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.22366 [quant-ph] (or arXiv:2603.22366v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2603.22366 Focus to learn more Submission history From: Devashish Chaudhary [view email] [v1] Mon, 23 Mar 2026 05:15:53 UTC (444 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI cs.LG References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

💬 Team Notes