Federated learning has gained popularity as a means of training models distributed across the wireless edge. The paper introduces delay-aware federated learning (DFL) to improve the efficiency of distributed machine learning (ML) model training by addressing communication delays between edge and cloud. DFL employs multiple stochastic gradient descent iterations on device datasets during each global aggregation interval and intermittently aggregates model parameters through edge servers in local subnetworks. The cloud server synchronizes the local models with the global deployed model computed via a local-global combiner at global synchronization. The convergence behavior of DFL is theoretically investigated under a generalized data heterogeneity metric. A set of conditions is obtained to achieve the sub-linear convergence rate of O(1/k). Based on these findings, an adaptive control algorithm is developed for DFL, implementing policies to mitigate energy consumption and edge-to-cloud communication latency while aiming for a sublinear convergence rate. Numerical evaluations show DFL's superior performance in terms of faster global model convergence, reduced resource consumption, and robustness against communication delays compared to existing FL algorithms. In summary, this proposed method offers improved efficiency and satisfactory results when dealing with both convex and non-convex loss functions.

翻译：联邦学习作为一种在无线边缘分布式训练模型的方法已受到广泛关注。本文提出时延感知联邦学习（DFL），通过解决边缘与云端之间的通信延迟问题，提升分布式机器学习（ML）模型训练效率。DFL在每个全局聚合间隔期间对设备数据集执行多次随机梯度下降迭代，并通过边缘服务器在局部子网中间歇性聚合模型参数。云端服务器在全局同步时，通过局部-全局组合器计算的全局部署模型与局部模型进行同步。基于广义数据异质性度量，本文从理论上研究了DFL的收敛行为，并获得了实现O(1/k)次线性收敛速率的一组条件。基于这些发现，为DFL开发了一种自适应控制算法，该算法在追求次线性收敛速率的同时，实施缓解能量消耗和边缘到云端通信延迟的策略。数值评估表明，与现有联邦学习算法相比，DFL在更快的全局模型收敛、降低资源消耗以及对通信延迟的鲁棒性方面表现出优越性能。综上所述，所提方法在处理凸损失函数和非凸损失函数时均能提升效率并取得令人满意的结果。