RobQFL: Robust Quantum Federated Learning in Adversarial Environment

2025/09/05 by Walid El Maouaki, Maouaki, Walid El, Nouhaila Innan +9
Biochemistry, Genetics and Molecular Biology · Computer Science · #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Spectroscopy Techniques in Biomedical and Chemical Research

paper · pdf · doi:10.48550/arxiv.2509.04914

openalex publication_date 2025/09/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Quantum Federated Learning (QFL) merges privacy-preserving federation with quantum computing gains, yet its resilience to adversarial noise is unknown. We first show that QFL is as fragile as centralized quantum learning. We propose Robust Quantum Federated Learning (RobQFL), embedding adversarial training directly into the federated loop. RobQFL exposes tunable axes: client coverage γ (0-100%), perturbation scheduling (fixed-ε vs ε-mixes), and optimization (fine-tune vs scratch), and distils the resulting γ× ε surface into two metrics: Accuracy-Robustness Area and Robustness Volume. On 15-client simulations with MNIST and Fashion-MNIST, IID and Non-IID conditions, training only 20-50% clients adversarially boosts ε ≤ 0.1 accuracy ∼15 pp at < 2 pp clean-accuracy cost; fine-tuning adds 3-5 pp. With ≥75% coverage, a moderate ε-mix is optimal, while high-ε schedules help only at 100% coverage. Label-sorted non-IID splits halve robustness, underscoring data heterogeneity as a dominant risk.

Citations

• Quantum Portfolio Optimization with Expert Analysis Evaluation
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Related

• QFAL: Quantum Federated Adversarial Learning
• Towards Heterogeneous Quantum Federated Learning: Challenges and Solutions
• Adversarial Robustness in Distributed Quantum Machine Learning
• When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
• Quantum Federated Learning: Architectural Elements and Future Directions