This study introduces the Quantum Federated Neural Network for Financial
Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine
Learning (QML) and quantum computing with Federated Learning (FL) to innovate
financial fraud detection. Using quantum technologies' computational power and
FL's data privacy, QFNN-FFD presents a secure, efficient method for identifying
fraudulent transactions. Implementing a dual-phase training model across
distributed clients surpasses existing methods in performance. QFNN-FFD
significantly improves fraud detection and ensures data confidentiality,
marking a significant advancement in fintech solutions and establishing a new
standard for privacy-focused fraud detection.

该研究介绍了量子联邦神经网络金融欺诈检测（QFNN-FFD）的创新框架，将量子机器学习（QML）和量子计算与联邦学习（FL）相结合，创新金融欺诈检测。通过利用量子技术的计算能力和联邦学习的数据隐私，QFNN-FFD 提供了一种安全高效的识别欺诈交易的方法。在分布式客户端上实现双阶段训练模型超越了现有方法的性能。QFNN-FFD 显著改进了欺诈检测并确保了数据保密性，标志着金融科技解决方案的重大进展，并为注重隐私的欺诈检测建立了新的标准。