mlsanityData to 14 Oct 2026

A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection

arXiv 2507.22908 ↗First posted 15 Jul 2025q-fin.CP cs.AI cs.LGcited by 7

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Abstract (arXiv)

Rapid growth of digital transactions has led to a surge in fraudulent activities, challenging traditional detection methods in the financial sector. To tackle this problem, we introduce a specialised federated learning framework that uniquely combines a quantum-enhanced Long Short-Term Memory (LSTM) model with advanced privacy preserving techniques. By integrating quantum layers into the LSTM architecture, our approach adeptly captures complex cross-transactional patters, resulting in an approximate 5% performance improvement across key evaluation metrics compared to conventional models. Central to our framework is "FedRansel", a novel method designed to defend against poisoning and inference attacks, thereby reducing model degradation and inference accuracy by 4-8%, compared to standard differential privacy mechanisms. This pseudo-centralised setup with a Quantum LSTM model, enhances fraud detection accuracy and reinforces the security and confidentiality of sensitive financial data.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 30 of its 61 references that are in the map. First posted on arXiv 15 Jul 2025.

Builds on

30 of 61 references.

References · 30

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Deep Learning with Differential Privacycited by 2,728
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  4. 2020Variational Quantum Algorithmscited by 692
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  6. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  7. 2019Can You Really Backdoor Federated Learning?cited by 255
  8. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  9. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 156
  10. 2020Quantum Long Short-Term Memorycited by 120
  11. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  12. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
18 more
  1. 2021Federated Quantum Machine Learningcited by 82
  2. 2020Local and Central Differential Privacy for Robustness and Privacy in Federated Learningcited by 78
  3. 2021Quantum Federated Learning with Quantum Datacited by 53
  4. 2021Quantum federated learning through blind quantum computingcited by 44
  5. 2023Financial Fraud Detection using Quantum Graph Neural Networkscited by 35
  6. 2024Membership Inference Attacks and Defenses in Federated Learning: A Surveycited by 31
  7. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  8. 2023Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Modelscited by 19
  9. 2024QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detectioncited by 18
  10. 2023Federated Quantum Machine Learning with Differential Privacycited by 17
  11. 2023Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecastingcited by 15
  12. 2023Federated Quantum Long Short-term Memory (FedQLSTM)cited by 15
  13. 2023Enhancing Quantum Support Vector Machines through Variational Kernel Trainingcited by 14
  14. 2023Simulation of a Variational Quantum Perceptron using Grover's Algorithmcited by 12
  15. 2024Advanced Financial Fraud Detection Using GNN-CL Modelcited by 11
  16. 2023Quantum Federated Learning With Quantum Networkscited by 8
  17. 2022Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settingscited by 7
  18. 2025QGAPHEnsemble : Combining Hybrid QLSTM Network Ensemble via Adaptive Weighting for Short Term Weather Forecastingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2025RobQFL: Robust Quantum Federated Learning in Adversarial Environmentcited by 2
  2. 2025Quantum Federated Learning: Architectural Elements and Future Directionscited by 2
  3. 2026Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Predictioncited by 2
  4. 2026Hybrid Quantum-Classical Neural Architecture Searchcited by 1
  5. 2026MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environmentscited by 0
  6. 2026Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physicscited by 0
  7. 2026Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detectioncited by 0