A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
Abhishek Sawaika, Swetang Krishna, Tushar Tomar, Durga Pritam Suggisetti, Aditi Lal, Tanmaya Shrivastav, Nouhaila Innan, Muhammad Shafique
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 specialized federated learning framework that uniquely combines a quantum-enhanced Long Short-Term Memory (LSTM) model with advanced privacy preservation techniques. By integrating quantum layers into the LSTM architecture, our approach adeptly captures complex cross-transactional patterns, 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4}-\mathbf{8 \%}$</tex>, compared to standard differential privacy mechanisms. This pseudo-centralized setup with a Quantum LSTM model, enhances fraud detection accuracy and reinforces the security and confidentiality of sensitive financial data.