Resource Consumption for Supporting Federated Learning in Wireless Networks

TL;DR

This paper develops an analytical model to understand how resource limitations in wireless networks affect federated learning performance, highlighting the trade-offs between accuracy, communication, and computing resources.

Contribution

It introduces a novel analytical framework that quantifies the relationship between resource consumption and model accuracy in wireless federated learning environments.

Findings

Model accurately predicts FL performance based on resources.

Trade-off analysis between communication and computing resources.

Validation through numerical simulations confirms theoretical insights.

Abstract

Federated learning (FL) has recently become one of the hottest focuses in wireless edge networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs train local machine learning models and transmit them to an aggregator, where a global model is formed and then sent back to UEs. In wireless networks, local training and model transmission can be unsuccessful due to constrained computing resources, wireless channel impairments, bandwidth limitations, etc., which degrades FL performance in model accuracy and/or training time. Moreover, we need to quantify the benefits and cost of deploying edge intelligence, as model training and transmission consume certain amount of resources. Therefore, it is imperative to deeply understand the relationship between FL performance and multiple-dimensional resources. In this paper, we construct an analytical model to investigate the relationship between the FL model accuracy and consumed resources in FL empowered wireless edge networks. Based on the analytical model, we explicitly quantify the model accuracy, available computing resources and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis, and demonstrate the trade-off between the communication and computing resources for achieving a certain model accuracy.