Abstract: Customized DL anomaly detection models and generated and deployed on disparate edge devices. Configuration-related information is fetched from the edge devices and, based on the configuration/capabilities of the edge device, at least one primary deep learning-based anomaly detection model is selected, which are customized based on the configuration/capabilities of the edge device. Customization involves limiting the volume of the predictors/variables and optimizing the iterations used to determine anomalies and/or make predictions. The customized models are subsequently packaged in edge device-specific formats, such as a customized set of binaries in C language or the like. The resulting customized DL anomaly detection application is subsequently deployed to the edge device where it is executable without the need for specialized hardware or communication with network entities, such as cloud nodes or servers.

Abstract: Customized DL anomaly detection models and generated and deployed on disparate edge devices. Configuration-related information is fetched from the edge devices and, based on the configuration/capabilities of the edge device, at least one master deep learning-based anomaly detection model is selected, which are customized based on the configuration/capabilities of the edge device. Customization involves limiting the volume of the predictors/variables and optimizing the iterations used to determine anomalies and/or make predictions. The customized models are subsequently packaged in edge device-specific formats, such as a customized set of binaries in C language or the like. The resulting customized DL anomaly detection application is subsequently deployed to the edge device where it is executable without the need for specialized hardware or communication with network entities, such as cloud nodes or servers.