The digital era has seen a marked increase in financial fraud. edge ML emerged as a promising solution for smartphone payment services fraud detection, enabling the deployment of ML models directly on edge devices. This approach enables a more personalized real-time fraud detection. However, a significant gap in current research is the lack of a robust system for monitoring data distribution shifts in these distributed edge ML applications. Our work bridges this gap by introducing a novel open-source framework designed for continuous monitoring of data distribution shifts on a network of edge devices. Our system includes an innovative calculation of the Kolmogorov-Smirnov (KS) test over a distributed network of edge devices, enabling efficient and accurate monitoring of users behavior shifts. We comprehensively evaluate the proposed framework employing both real-world and synthetic financial transaction datasets and demonstrate the framework's effectiveness.

翻译：数字时代金融欺诈显著增加。边缘机器学习作为智能手机支付服务欺诈检测的一种有前景的解决方案，支持将机器学习模型直接部署在边缘设备上。这种方法能够实现更个性化的实时欺诈检测。然而，当前研究中的一个显著空白是缺乏一个鲁棒的系统来监控这些分布式边缘机器学习应用中的数据分布漂移。我们的工作通过引入一个新颖的开源框架来弥补这一空白，该框架专为在边缘设备网络上持续监控数据分布漂移而设计。我们的系统包含一种创新的分布式边缘设备网络上的Kolmogorov-Smirnov（KS）检验计算方法，能够高效且准确地监控用户行为漂移。我们使用真实世界和合成的金融交易数据集对所提出的框架进行了全面评估，并展示了该框架的有效性。