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.
翻译:联邦学习联合会(FL)已成为6G中基于AI的服务、应用和网络程序分布培训的有希望的框架。 影响6G无线FL系统性能和效率的主要挑战之一是将用户装置安排在资源受限制的频道上。在这项工作中,我们认为,FL客户装置的升级联系安排是一个关系结构丰富的问题。为了应对这一挑战,我们提出了一个创新的、节能的和有重要认识的衡量标准,通过利用不受监督的图表代表学习(UGRL),将客户安排在FL应用程序中。我们提议的方法在排期过程中引入了一种关系引导偏差,不需要从客户装置收集培训反馈信息,这与最先进的重要性意识机制不同。我们根据文献中最近提出的指标,对照基线排期算法评估了我们提出的解决办法。结果显示,在考虑显示空间关系的节点假设时,我们的方法在模型精度和能源效率方面平均可达到10%,比最先进的重要性政策高出17倍。