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.

数字化时代见证了金融欺诈的显著增长。我们的研究填补了当前研究中在分布式边缘机器学习应用中对数据分布漂移的监测缺乏稳健系统的重要空白。通过引入一种创新的开源框架，我们的系统能够连续监测网络上边缘设备上的数据分布漂移，包括在分布式网络上进行 Kolmogorov-Smirnov（KS）测试的创新计算，从而有效准确地监测用户行为变化。我们对提出的框架进行了综合评估，使用真实和合成的金融交易数据集，并展示了该框架的有效性。

边缘机器学习欺诈检测中的数据分布变化的分布式监控

Distributed Monitoring for Data Distribution Shifts in Edge-ML Fraud  Detection

Computer vision systems for automatic image categorization have become
accurate and reliable enough that they can run continuously for days or even
years as components of real-world commercial applications. A major open problem
in this context, however, is quality control. Good classification performance
can only be expected if systems run under the specific conditions, in
particular data distributions, that they were trained for. Surprisingly, none
of the currently used deep network architectures has a built-in functionality
that could detect if a network operates on data from a distribution that it was
not trained for and potentially trigger a warning to the human users. In this
work, we describe KS(conf), a procedure for detecting such outside of the
specifications operation. Building on statistical insights, its main step is
the applications of a classical Kolmogorov-Smirnov test to the distribution of
predicted confidence values. We show by extensive experiments using ImageNet,
AwA2 and DAVIS data on a variety of ConvNets architectures that KS(conf)
reliably detects out-of-specs situations. It furthermore has a number of
properties that make it an excellent candidate for practical deployment: it is
easy to implement, adds almost no overhead to the system, works with all
networks, including pretrained ones, and requires no a priori knowledge about
how the data distribution could change.

描述 KS（conf）的程序来检测计算机视觉系统在训练数据分布之外的情况下的操作，主要步骤是对预测置信值的分布应用经典的 Kolmogorov-Smirnov 检验，研究表明此程序可靠地检测到规格外的情况。

KS (conf): 一种轻量级的测试方法，用于检测 ConvNet 是否在其规格之外运作

KS(conf ): A Light-Weight Test if a ConvNet Operates Outside of Its  Specifications

We study a novel spline-like basis, which we name the "falling factorial
basis", bearing many similarities to the classic truncated power basis. The
advantage of the falling factorial basis is that it enables rapid, linear-time
computations in basis matrix multiplication and basis matrix inversion. The
falling factorial functions are not actually splines, but are close enough to
splines that they provably retain some of the favorable properties of the
latter functions. We examine their application in two problems: trend filtering
over arbitrary input points, and a higher-order variant of the two-sample
Kolmogorov-Smirnov test.

我们研究了一种新的类似于样条基函数的基函数，名为 “下降阶乘基函数”，它与经典的截断幂基函数有很多相似之处。 下降阶乘基函数的优点是在基矩阵乘法和基矩阵求逆中实现快速的线性时间计算。 我们研究了这个基函数在趋势过滤和两个样本 Kolmogorov-Smirnov 测试的高阶变体中的应用。