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

Abstract

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.

To be published in proceedings of IEEE International Conference on Quantum Computing and Engineering (QCE) 2025

References in corpus (5)

• Financial Fraud Detection using Quantum Graph Neural Networks
• Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models
• FedQNN: Federated Learning using Quantum Neural Networks
• Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting
• QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection

Cited by in corpus (1)

• Evaluating Quantum Amplitude Estimation for Pricing Multi-Asset Basket Options