Distributed Learning using Consensus on Edge AI

Abstract

Moving machine learning services such as inference and training from the cloud layer to the edge layer is a complex task, but necessary to guarantee the quality of service of many Internet of Things (IoT) applications. However, running machine learning models in edge computing using lighter (limited) hardware ends up being an obstacle to applying powerful models that have better accuracy. In this context, distributed machine learning techniques aim to mitigate such limitations, being federated learning, model compression and model ensemble some of the existing alternatives. The present work proposes a new distributed machine learning technique focused on inference, which improves the accuracy of the final response of the models respecting the limitations of commonly used hardware in edge computing through a consensus algorithm.