mlsanityData to 7 Oct 2026

QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection

arXiv 2404.02595 ↗First posted 3 Apr 2024quant-ph cs.LG q-fin.RMcited by 18

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Abstract (arXiv)

This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine Learning (QML) and quantum computing with Federated Learning (FL) for financial fraud detection. Using quantum technologies' computational power and the robust data privacy protections offered by FL, QFNN-FFD emerges as a secure and efficient method for identifying fraudulent transactions within the financial sector. Implementing a dual-phase training model across distributed clients enhances data integrity and enables superior performance metrics, achieving precision rates consistently above 95%. Additionally, QFNN-FFD demonstrates exceptional resilience by maintaining an impressive 80% accuracy, highlighting its robustness and readiness for real-world applications. This combination of high performance, security, and robustness against noise positions QFNN-FFD as a transformative advancement in financial technology solutions and establishes it as a new benchmark for privacy-focused fraud detection systems. This framework facilitates the broader adoption of secure, quantum-enhanced financial services and inspires future innovations that could use QML to tackle complex challenges in other areas requiring high confidentiality and accuracy.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 22 of its 51 references that are in the map. First posted on arXiv 3 Apr 2024.

Builds on

22 of 51 references.

References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  3. 2020The power of quantum neural networkscited by 377
  4. 2020Power of data in quantum machine learningcited by 336
  5. 2016Quantum Machine Learningcited by 325
  6. 2019Efficient Learning for Deep Quantum Neural Networkscited by 160
  7. 2021Federated Quantum Machine Learningcited by 82
  8. 2021Quantum Federated Learning with Quantum Datacited by 53
  9. 2021Quantum federated learning through blind quantum computingcited by 44
  10. 2023Financial Fraud Detection using Quantum Graph Neural Networkscited by 35
  11. 2023A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Aheadcited by 29
  12. 2022Slimmable Quantum Federated Learningcited by 23
10 more
  1. 2022Federated Learning with Quantum Secure Aggregationcited by 21
  2. 2023Foundations of Quantum Federated Learning Over Classical and Quantum Networkscited by 21
  3. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  4. 2023Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Modelscited by 19
  5. 2022Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selectioncited by 15
  6. 2022Non-IID Quantum Federated Learning with One-shot Communication Complexitycited by 15
  7. 2023Enhancing Quantum Support Vector Machines through Variational Kernel Trainingcited by 14
  8. 2023Simulation of a Variational Quantum Perceptron using Grover's Algorithmcited by 12
  9. 2022A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithmscited by 7
  10. 2023Quantum State Tomography using Quantum Machine Learningcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 8; 2026: 5.

18 papers.

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  1. 2024Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detectioncited by 10
  2. 2024Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving MLcited by 8
  3. 2024LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networkscited by 8
  4. 2024MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryptioncited by 7
  5. 2025A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAGcited by 7
  6. 2025A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detectioncited by 7
  7. 2025Quantum Federated Learning: A Comprehensive Surveycited by 7
  8. 2024Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sourcescited by 3
  9. 2025Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detectioncited by 2
  10. 2025RobQFL: Robust Quantum Federated Learning in Adversarial Environmentcited by 2
  11. 2025Towards Quantum-Ready Blockchain Fraud Detection via Ensemble Graph Neural Networkscited by 2
  12. 2026Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Predictioncited by 2
6 more
  1. 2025IQNN-CS: Interpretable Quantum Neural Network for Credit Scoringcited by 1
  2. 2025Quantum Federated Learning: Architectural Elements and Future Directionscited by 1
  3. 2026Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detectioncited by 0
  4. 2026Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correctioncited by 0
  5. 2026A Drift Stable Quantum Federated Learning for Intelligent Servicescited by 0
  6. 2026Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approachcited by 0