Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning

TL;DR

This paper introduces an importance-aware uplink scheduling method for federated learning in 6G networks, utilizing graph representation learning to improve energy efficiency and model accuracy without requiring feedback from devices.

Contribution

It proposes a novel, feedback-free, importance-aware scheduling metric based on unsupervised graph representation learning for federated learning in resource-constrained wireless environments.

Findings

Achieves up to 10% higher model accuracy.

Up to 17 times better energy efficiency.

Effective in scenarios with spatial relations among nodes.

Abstract

Federated Learning (FL) has emerged as a promising framework for distributed training of AI-based services, applications, and network procedures in 6G. One of the major challenges affecting the performance and efficiency of 6G wireless FL systems is the massive scheduling of user devices over resource-constrained channels. In this work, we argue that the uplink scheduling of FL client devices is a problem with a rich relational structure. To address this challenge, we propose a novel, energy-efficient, and importance-aware metric for client scheduling in FL applications by leveraging Unsupervised Graph Representation Learning (UGRL). Our proposed approach introduces a relational inductive bias in the scheduling process and does not require the collection of training feedback information from client devices, unlike state-of-the-art importance-aware mechanisms. We evaluate our proposed solution against baseline scheduling algorithms based on recently proposed metrics in the literature. Results show that, when considering scenarios of nodes exhibiting spatial relations, our approach can achieve an average gain of up to 10% in model accuracy and up to 17 times in energy efficiency compared to state-of-the-art importance-aware policies.