Optimised statistical model updates in distributed intelligence environments

Abstract

This paper explores a sequential decision making methodology of when to update statistical learning models in Intelligent Edge Computing devices given underlying changes in the contextual data distribution. The proposed model update scheduling takes into consideration the optimal decision time for minimizing the network overhead while preserving the prediction accuracy of the models. The paper reports on a comparison between the proposed approach with four other update delaying policies found in the literature, an evaluation of the performances using linear and support vector regression models over real contextual data streams and a discussion on the strengths and weaknesses of the proposed policy.