Theory of Mixture-of-Experts for Mobile Edge Computing

TL;DR

This paper introduces a mixture-of-experts framework tailored for mobile edge computing to improve continual learning by dynamically routing tasks to specialized servers, reducing generalization error over time.

Contribution

It pioneers the application of MoE theory in MEC networks for online task streams, with an adaptive gating network and theoretical analysis of expert requirements.

Findings

MoE reduces generalization error over time in MEC.

Optimal number of experts ensures convergence and task specialization.

Adding excess experts can delay convergence and increase error.

Abstract

In mobile edge computing (MEC) networks, mobile users generate diverse machine learning tasks dynamically over time. These tasks are typically offloaded to the nearest available edge server, by considering communication and computational efficiency. However, its operation does not ensure that each server specializes in a specific type of tasks and leads to severe overfitting or catastrophic forgetting of previous tasks. To improve the continual learning (CL) performance of online tasks, we are the first to introduce mixture-of-experts (MoE) theory in MEC networks and save MEC operation from the increasing generalization error over time. Our MoE theory treats each MEC server as an expert and dynamically adapts to changes in server availability by considering data transfer and computation time. Unlike existing MoE models designed for offline tasks, ours is tailored for handling continuous streams of tasks in the MEC environment. We introduce an adaptive gating network in MEC to adaptively identify and route newly arrived tasks of unknown data distributions to available experts, enabling each expert to specialize in a specific type of tasks upon convergence. We derived the minimum number of experts required to match each task with a specialized, available expert. Our MoE approach consistently reduces the overall generalization error over time, unlike the traditional MEC approach. Interestingly, when the number of experts is sufficient to ensure convergence, adding more experts delays the convergence time and worsens the generalization error. Finally, we perform extensive experiments on real datasets in deep neural networks (DNNs) to verify our theoretical results.