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

TL;DR

This paper presents a federated learning framework that integrates quantum-enhanced LSTM models and privacy-preserving techniques to improve financial fraud detection accuracy and security.

Contribution

It introduces a novel hybrid quantum-classical federated framework with FedRansel for enhanced privacy and robustness against attacks in financial fraud detection.

Findings

Achieved approximately 5% performance improvement over traditional models.

FedRansel reduces model degradation and inference errors by 4-8%.

Quantum-enhanced LSTM captures complex transaction patterns effectively.

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.