DISTRIBUTED MACHINE LEARNING PLATFORM USING FOG COMPUTING

摘要

Systems and methods involving distributed machine learning using fog computing are described. The distributed machine learning architecture described involves at least a cloud server, one or more fog nodes and one or more edge devices. The cloud server has superior computational power compared to the fog nodes and edge devices and the edge devices may have inferior computational power compared to the fog nodes. The cloud server, fog nodes and edge devices may each have machine learning capability involving learning algorithms used to train models that may be used for inferencing. The distributed machine learning platform described herein may be used for making predictions and identifying certain types of data or trends in data. By distributing the machine learning computation to lower level devices, such as fog nodes and edge devices, bandwidth usage and latency common in traditional distributed systems may be reduced.