In this work, we consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration. We formulate a stochastic network optimization problem for designing a dynamic scheduling policy that maximizes the time-average data importance from scheduled user sets subject to energy consumption and latency constraints. Our proposed algorithm based on the Lyapunov optimization framework outperforms alternative methods without considering time-varying data importance, especially when the generation of training data shows strong temporal correlation.

翻译：本文考虑一个联邦边缘学习系统，其中训练数据随时间在多个具有长期能量约束的分布式边缘设备上随机生成。由于通信资源和延迟需求的限制，每一轮迭代中仅调度部分设备参与本地训练过程。我们构建了一个随机网络优化问题，旨在设计一种动态调度策略，在满足能量消耗和延迟约束的条件下，最大化调度用户集的时间平均数据重要性。基于李雅普诺夫优化框架提出的算法优于未考虑时变数据重要性的替代方法，特别是在训练数据生成呈现强时间相关性时，性能优势更为显著。