  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.
