Collaborating between Local and Global Learning for Distributed Online Multiple Tasks

Abstract

This paper studies the novel learning scenarios of Distributed Online Multi-tasks (DOM), where the learning individuals with continuously arriving data are distributed separately and meanwhile they need to learn individual models collaboratively. It has three characteristics: distributed learning, online learning and multi-task learning. It is motivated by the emerging applications of wearable devices, which aim to provide intelligent monitoring services, such as health emergency alarming and movement recognition.