Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning
This addresses the problem of efficient device scheduling in resource-constrained 6G wireless FL systems, offering an incremental improvement over existing methods.
The paper tackles the challenge of uplink scheduling for client devices in Federated Learning over 6G networks by proposing an importance-aware metric using Unsupervised Graph Representation Learning, achieving up to 10% higher model accuracy and 17 times better energy efficiency compared to state-of-the-art policies.
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.