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

Edge Machine Learning (Edge ML), which shifts computational intelligence from
cloud-based systems to edge devices, is attracting significant interest due to
its evident benefits including reduced latency, enhanced data privacy, and
decreased connectivity reliance. While these advantages are compelling, they
introduce unique challenges absent in traditional cloud-based approaches. In
this paper, we delve into the intricacies of Edge-based learning, examining the
interdependencies among: (i) constrained data storage on Edge devices, (ii)
limited computational power for training, and (iii) the number of learning
classes. Through experiments conducted using our MAGNETO system, that focused
on learning human activities via data collected from mobile sensors, we
highlight these challenges and offer valuable perspectives on Edge ML.

利用移动传感器收集的数据，通过我们的 MAGNETO 系统以学习人类活动为重点的实验，我们深入研究了边缘机器学习（Edge ML）的复杂性，考察了边缘设备上受限数据存储、训练的有限计算能力和学习类别数量之间的相互依赖关系，并提供了关于 Edge ML 的有价值的观点。

关于边缘端实时学习新型人类体力活动的实用经验

Practical Insights on Incremental Learning of New Human Physical  Activity on the Edge

Fueled by the availability of more data and computing power, recent
breakthroughs in cloud-based machine learning (ML) have transformed every
aspect of our lives from face recognition and medical diagnosis to natural
language processing. However, classical ML exerts severe demands in terms of
energy, memory and computing resources, limiting their adoption for resource
constrained edge devices. The new breed of intelligent devices and high-stake
applications (drones, augmented/virtual reality, autonomous systems, etc.),
requires a novel paradigm change calling for distributed, low-latency and
reliable ML at the wireless network edge (referred to as edge ML). In edge ML,
training data is unevenly distributed over a large number of edge nodes, which
have access to a tiny fraction of the data. Moreover training and inference is
carried out collectively over wireless links, where edge devices communicate
and exchange their learned models (not their private data). In a first of its
kind, this article explores key building blocks of edge ML, different neural
network architectural splits and their inherent tradeoffs, as well as
theoretical and technical enablers stemming from a wide range of mathematical
disciplines. Finally, several case studies pertaining to various high-stake
applications are presented demonstrating the effectiveness of edge ML in
unlocking the full potential of 5G and beyond.

本文探讨了边缘机器学习的关键构建块，神经网络架构的不同分裂及其内在的权衡，以及来自广泛数学学科的理论和技术促进因素，最终呈现了几个关于各种高风险应用的案例研究，展示了边缘机器学习在发挥 5G 及其以后的全部潜力方面的有效性。