Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks

TL;DR

This paper introduces an age-based scheduling policy for federated learning in mobile edge networks, optimizing communication efficiency by considering parameter staleness and channel quality.

Contribution

It proposes a novel scheduling algorithm based on age of update (AoU) that jointly considers staleness and channel conditions to enhance federated learning performance.

Findings

The proposed algorithm improves training efficiency in simulations.

It effectively balances parameter freshness and channel quality.

The method has low computational complexity.

Abstract

Federated learning (FL) is a machine learning model that preserves data privacy in the training process. Specifically, FL brings the model directly to the user equipments (UEs) for local training, where an edge server periodically collects the trained parameters to produce an improved model and sends it back to the UEs. However, since communication usually occurs through a limited spectrum, only a portion of the UEs can update their parameters upon each global aggregation. As such, new scheduling algorithms have to be engineered to facilitate the full implementation of FL. In this paper, based on a metric termed the age of update (AoU), we propose a scheduling policy by jointly accounting for the staleness of the received parameters and the instantaneous channel qualities to improve the running efficiency of FL. The proposed algorithm has low complexity and its effectiveness is demonstrated by Monte Carol simulations.