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

Paper: arXiv 2507.22908

Authors: 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 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.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematical concepts, including quantum machine learning layers and differential privacy formulations, indicating high mathematical complexity. While the abstract presents specific quantitative results (e.g., 5% improvement, 4-8% reduction in degradation), the excerpt lacks detailed implementation code or backtest specifics, placing empirical rigor in the moderate range.

Research Flowchart

  flowchart TD
  A["Research Goal: Enhance financial fraud detection with privacy-preserving federated learning and quantum AI."] --> B["Methodology: Hybrid Quantum-Enhanced LSTM in a Federated Framework"]
  B --> C["Input: Distributed Financial Transaction Data"]
  C --> D["Process: Quantum LSTM Feature Extraction & Training"]
  D --> E["Process: FedRansel Privacy Preservation<br>(Defense vs. Poisoning/Inference Attacks)"]
  E --> F{"Outcome: Evaluation Metrics"}
  F --> G["Result 1: ~5% Improvement in Fraud Detection Accuracy"]
  F --> H["Result 2: 4-8% Reduction in Model Degradation/Inference Risk"]