Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment

TL;DR

This paper introduces a learning-centric wireless resource allocation scheme for edge computing that optimizes training performance across heterogeneous tasks, validated through simulations and real-world experiments.

Contribution

It proposes a novel LCWRA scheme that accounts for task heterogeneity and environment mismatch, improving learning performance in edge networks.

Findings

Optimal transmission time inversely related to generalization error

LCWRA outperforms existing methods in simulations

Robust performance demonstrated in real-world experiments

Abstract

Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fundamental communication question is: how to allocate the limited wireless resources (such as time, energy) to the simultaneous model training of heterogeneous learning tasks? Existing methods ignore two important facts: 1) different models have heterogeneous demands on training data; 2) there is a mismatch between the simulated environment and the real-world environment. As a result, they could lead to low learning performance in practice. This paper proposes the learning centric wireless resource allocation (LCWRA) scheme that maximizes the worst learning performance of multiple tasks. Analysis shows that the optimal transmission time has an inverse power relationship with respect to the generalization error. Finally, both simulation and experimental results are provided to verify the performance of the proposed LCWRA scheme and its robustness in real implementation.