Federated learning aims to construct a global model that fits the dataset distributed across local devices without direct access to private data, leveraging communication between a server and the local devices. In the context of a practical communication scheme, we study the completion time required to achieve a target performance. Specifically, we analyze the number of iterations required for federated learning to reach a specific optimality gap from a minimum global loss. Subsequently, we characterize the time required for each iteration under two fundamental multiple access schemes: time-division multiple access (TDMA) and random access (RA). We propose a step-wise batch allocation, demonstrated to be optimal for TDMA-based federated learning systems. Additionally, we show that the non-zero batch gap between devices provided by the proposed step-wise batch allocation significantly reduces the completion time for RA-based learning systems. Numerical evaluations validate these analytical results through real-data experiments, highlighting the remarkable potential for substantial completion time reduction.

翻译：联邦学习旨在通过服务器与本地设备之间的通信，在不直接访问私有数据的情况下构建适配各设备分布式数据集的全局模型。结合实际通信方案，我们研究了实现目标性能所需的完成时间。具体而言，我们分析了联邦学习从最小全局损失达到特定最优性差距所需的迭代次数。随后，我们在两种基本多址接入方案——时分多址（TDMA）与随机接入（RA）——下推导了单次迭代所需的时间。我们提出了一种分步批次分配方法，并证明该方法对于基于TDMA的联邦学习系统具有最优性。此外，研究表明，所提出的分步批次分配产生的设备间非零批次差距可显著缩短基于RA的学习系统的完成时间。数值评估通过真实数据实验验证了这些分析结果，突显了大幅降低完成时间的显著潜力。