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

arXiv: https://arxiv.org/abs/2401.05219 · HTML: https://mlsanity.com/paper/2401.05219/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Computing Extremely Accurate Quantiles Using t-Digests](https://mlsanity.com/paper/1902.04023/) (2019)
- [Monitoring and Adapting ML Models on Mobile Devices](https://mlsanity.com/paper/2305.07772/) (2023)

## Built on by (most cited first, 1 of 1)

- [Output Length Effect on DeepSeek-R1's Safety in Forced Thinking](https://mlsanity.com/paper/2503.01923/) (2025)

## Abstract (arXiv)

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.