A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
This addresses fraud detection in the financial sector with incremental improvements in accuracy and security.
The paper tackles financial fraud detection by introducing a federated learning framework with a quantum-enhanced LSTM model and privacy-preserving techniques, achieving approximately 5% performance improvement and reducing model degradation by 4-8% compared to conventional methods.
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.