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

arXiv: https://arxiv.org/abs/2507.22908 · HTML: https://mlsanity.com/paper/2507.22908/

## 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 (most cited first, 30 of 30)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Can You Really Backdoor Federated Learning?](https://mlsanity.com/paper/1911.07963/) (2019)
- [Evaluating Gradient Inversion Attacks and Defenses in Federated Learning](https://mlsanity.com/paper/2112.00059/) (2021)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Quantum Long Short-Term Memory](https://mlsanity.com/paper/2009.01783/) (2020)
- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Local and Central Differential Privacy for Robustness and Privacy in Federated Learning](https://mlsanity.com/paper/2009.03561/) (2020)
- [Quantum Federated Learning with Quantum Data](https://mlsanity.com/paper/2106.00005/) (2021)
- [Quantum federated learning through blind quantum computing](https://mlsanity.com/paper/2103.08403/) (2021)
- [Financial Fraud Detection using Quantum Graph Neural Networks](https://mlsanity.com/paper/2309.01127/) (2023)
- [Membership Inference Attacks and Defenses in Federated Learning: A Survey](https://mlsanity.com/paper/2412.06157/) (2024)
- [FedQNN: Federated Learning using Quantum Neural Networks](https://mlsanity.com/paper/2403.10861/) (2024)
- [Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models](https://mlsanity.com/paper/2308.05237/) (2023)
- [QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection](https://mlsanity.com/paper/2404.02595/) (2024)
- [Federated Quantum Machine Learning with Differential Privacy](https://mlsanity.com/paper/2310.06973/) (2023)
- [Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting](https://mlsanity.com/paper/2310.17032/) (2023)
- [Federated Quantum Long Short-term Memory (FedQLSTM)](https://mlsanity.com/paper/2312.14309/) (2023)
- [Enhancing Quantum Support Vector Machines through Variational Kernel Training](https://mlsanity.com/paper/2305.06063/) (2023)
- [Simulation of a Variational Quantum Perceptron using Grover's Algorithm](https://mlsanity.com/paper/2305.11040/) (2023)
- [Advanced Financial Fraud Detection Using GNN-CL Model](https://mlsanity.com/paper/2407.06529/) (2024)
- [Quantum Federated Learning With Quantum Networks](https://mlsanity.com/paper/2310.15084/) (2023)
- [Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings](https://mlsanity.com/paper/2206.00807/) (2022)
- [QGAPHEnsemble : Combining Hybrid QLSTM Network Ensemble via Adaptive Weighting for Short Term Weather Forecasting](https://mlsanity.com/paper/2501.10866/) (2025)

## Built on by (most cited first, 7 of 7)

- [RobQFL: Robust Quantum Federated Learning in Adversarial Environment](https://mlsanity.com/paper/2509.04914/) (2025)
- [Quantum Federated Learning: Architectural Elements and Future Directions](https://mlsanity.com/paper/2510.17642/) (2025)
- [Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction](https://mlsanity.com/paper/2601.03802/) (2026)
- [Hybrid Quantum-Classical Neural Architecture Search](https://mlsanity.com/paper/2605.18345/) (2026)
- [MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments](https://mlsanity.com/paper/2604.11131/) (2026)
- [Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics](https://mlsanity.com/paper/2604.15775/) (2026)
- [Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection](https://mlsanity.com/paper/2605.21164/) (2026)

## 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.