  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.
