QFNN-FFD:用于金融欺诈检测的量子联邦神经网络

Nouhaila Innan, Alberto Marchisio, Muhammad Shafique, Mohamed Bennai
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引用次数: 0

摘要

本研究介绍了用于金融欺诈检测的量子联邦神经网络(QFNN-FFD),这是一个将量子机器学习(QML)和量子计算与联邦学习(FL)相结合的前沿框架,用于创新金融欺诈检测。利用量子技术的计算能力和联邦学习的数据隐私性,QFNN-FFD 提出了一种安全、高效的方法来识别欺诈交易。在分布式客户端上实施双阶段训练模型,在性能上超越了现有方法。QFNN-FFD 显著提高了欺诈检测能力,确保了数据的保密性,标志着金融技术解决方案的重大进步,并为注重隐私的欺诈检测建立了新标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
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.
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