mlsanityData to 14 Oct 2026

RobQFL: Robust Quantum Federated Learning in Adversarial Environment

arXiv 2509.04914 ↗First posted 5 Sep 2025quant-ph cs.LGcited by 2

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Abstract (arXiv)

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-varepsilon vs varepsilon-mixes), and optimization (fine-tune vs scratch), and distils the resulting γ × varepsilon 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 varepsilon ≤ 0.1 accuracy ~15 pp at < 2 pp clean-accuracy cost; fine-tuning adds 3-5 pp. With ≥75% coverage, a moderate varepsilon-mix is optimal, while high-varepsilon schedules help only at 100% coverage. Label-sorted non-IID splits halve robustness, underscoring data heterogeneity as a dominant risk.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 34 of its 55 references that are in the map. First posted on arXiv 5 Sep 2025.

Builds on

34 of 55 references.

References · 34

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  3. 2013Intriguing properties of neural networkscited by 5,155
  4. 2018Robustness May Be at Odds with Accuracycited by 880
  5. 2020The power of quantum neural networkscited by 379
  6. 2016Quantum Machine Learningcited by 326
  7. 2023Challenges and Opportunities in Quantum Machine Learningcited by 203
  8. 2021Federated Quantum Machine Learningcited by 82
  9. 2019Quantum Adversarial Machine Learningcited by 67
  10. 2021Quantum federated learning through blind quantum computingcited by 44
  11. 2023Financial Fraud Detection using Quantum Graph Neural Networkscited by 35
  12. 2023A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Aheadcited by 29
22 more
  1. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  2. 2023Delving into the Adversarial Robustness of Federated Learningcited by 19
  3. 2023Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Modelscited by 19
  4. 2024QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detectioncited by 18
  5. 2023Federated Quantum Machine Learning with Differential Privacycited by 17
  6. 2023Privacy-preserving quantum federated learning via gradient hidingcited by 15
  7. 2022Federated Quantum Natural Gradient Descent for Quantum Federated Learningcited by 14
  8. 2023Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descentcited by 14
  9. 2023Enhancing Quantum Support Vector Machines through Variational Kernel Trainingcited by 14
  10. 2023Simulation of a Variational Quantum Perceptron using Grover's Algorithmcited by 12
  11. 2024Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detectioncited by 10
  12. 2025HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Predictioncited by 10
  13. 2024LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networkscited by 9
  14. 2024Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving MLcited by 8
  15. 2024Quantum delegated and federated learning via quantum homomorphic encryptioncited by 7
  16. 2024MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryptioncited by 7
  17. 2025A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detectioncited by 7
  18. 2024Quantum Clustering for Cybersecuritycited by 6
  19. 2025QFAL: Quantum Federated Adversarial Learningcited by 6
  20. 2024Brain Tumor Diagnosis Using Quantum Convolutional Neural Networkscited by 4
  21. 2024SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systemscited by 4
  22. 2024Quantum Support Vector Machine for Prostate Cancer Detection: A Performance Analysiscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Hybrid Quantum-Classical Neural Architecture Searchcited by 1
  2. 2026Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networkscited by 0