Federated and Robust Quantum Generative Adversarial Networks for Anomaly Detection in Future Internet
Coach Name
Jordi Bosch i Garcia
EU Organization
University of Salerno (UNISA, Italy)
Members
- Prof. Christian Esposito
- Prof. Sadok Ben Yahia
- Dr. Franco Cirillo
- Dr. Biagio Boi
- Dr. Marco De Santis
CA Organization
École de Technologie Supérieure (ÉTS-Montréal, Canada)
Members
- Prof. Rami Langar
- Prof. Wael Jaafar
- Hakim Najiullah
- Dr. Jacques McNeill (Numana)
Project Overview
FRQGAN4AD explores how Quantum Generative Adversarial Networks (QGANs) can revolutionize anomaly detection in network traffic, helping to safeguard Next-Generation Internet infrastructures from evolving cyber threats.
The project developed a federated quantum machine-learning framework capable of detecting anomalies across distributed networks without centralizing sensitive data. It combines quantum AI, federated learning, and blockchain-based trust mechanisms, ensuring privacy, scalability, and transparency.
Experiments were conducted using both quantum simulators and real quantum hardware, validating the feasibility of quantum models for cybersecurity. The system was benchmarked on public intrusion-detection datasets (e.g., NSL-KDD) and demonstrated accuracy and robustness comparable to classical deep-learning baselines, even under quantum noise conditions.
Methods and approaches
Federated Quantum GAN Architecture
FRQGAN4AD designed a hybrid quantum–classical GAN where quantum generators and classical discriminators collaboratively learn attack patterns. A federated architecture enables decentralized model training, keeping data local and enhancing privacy while maintaining detection accuracy.
Blockchain and Quantum Device Attestation (QPUF)
Blockchain technology was integrated for secure coordination among federated nodes, with Quantum Physical Unclonable Functions (QPUFs) verifying the authenticity of quantum devices. This ensures tamper-resistant logging and trusted model updates across the distributed system.
Key Achievements
Developed and validated a robust Quantum GAN for intrusion detection on simulated and real quantum hardware.
Implemented a Federated QGAN enabling privacy-preserving distributed anomaly detection.
Integrated blockchain and QPUF-based attestation to enhance transparency and trust.
Released open-source repositories for QGAN and federated QGAN implementations:
Published six peer-reviewed papers in leading IEEE and AI-for-Quantum venues (QCNC 2025, ICDCS 2025, QCE 2025).
Built a project website: https://frqgan4ad.github.io.
Impact & Results
Scientific Impact
FRQGAN4AD demonstrates that quantum-enhanced AI can outperform or match classical methods for anomaly detection, paving the way for quantum cybersecurity research.
Strategic Impact
Contributes to Europe’s long-term vision for quantum-secure Next-Generation Internet infrastructures.
Economic Impact
Lays the foundation for quantum-based cybersecurity products applicable to telecom, finance, and cloud infrastructures.
Social Impact
Strengthens resilience of digital ecosystems and supports privacy-preserving international collaboration between Europe and Canada.
Publications and Open-Source Contributions
- Publications (IEEE / AIQxQIA 2025):
- Federated Quantum GAN for Intrusion Detection – IEEE ICDCS 2025
- QPUF-based Secure Quantum Device Attestation – IEEE QCNC 2025
- Quantum Machine Learning for Intrusion Detection on Noisy Devices – IEEE QCE 2025
- Quantum Autoencoder for Cyberattack Detection in Smart Power Systems – AIQxQIA 2025
- Federated Quantum GAN for Intrusion Detection – IEEE ICDCS 2025
- Open repositories:
- Website: https://frqgan4ad.github.io
- LinkedIn updates: FRQGAN4AD Project
Future directions
The consortium plans to:
Integrate secure model aggregation into the federated QGAN for enhanced privacy and resilience.