In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in which wireless users transmit their local FL models (trained using their locally collected data) to a base station (BS). The BS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all users. Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step. Moreover, since each user has unique training data samples, the BS prefers to include all local user FL models to generate a converged global FL model. Hence, the FL performance and convergence time will be significantly affected by the user selection scheme. Therefore, it is necessary to design an appropriate user selection scheme that enables users of higher importance to be selected more frequently. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize the FL convergence time while optimizing the FL performance. To solve this problem, a probabilistic user selection scheme is proposed such that the BS is connected to the users whose local FL models have significant effects on its global FL model with high probabilities. Given the user selection policy, the uplink RB allocation can be determined. To further reduce the FL convergence time, artificial neural networks (ANNs) are used to estimate the local FL models of the users that are not allocated any RBs for local FL model transmission at each given learning step, which enables the BS to enhance its global FL model and improve the FL convergence speed and performance.

中文翻译：

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无线网络联合学习的收敛时间优化

在本文中，研究了在实际无线网络上部署时联邦学习 (FL) 的收敛时间。特别地，考虑无线网络，其中无线用户将他们的本地FL模型（使用他们本地收集的数据训练）传输到基站（BS）。BS 作为中央控制器，使用接收到的本地 FL 模型生成全局 FL 模型，并将其广播回所有用户。由于无线网络中资源块 (RB) 的数量有限，因此在每个学习步骤中只能选择一部分用户将其本地 FL 模型参数传输到 BS。此外，由于每个用户都有唯一的训练数据样本，因此 BS 更喜欢包含所有本地用户的 FL 模型来生成收敛的全局 FL 模型。因此，FL 性能和收敛时间将受到用户选择方案的显着影响。因此，有必要设计一个合适的用户选择方案，使重要性更高的用户能够被更频繁地选择。这种联合学习、无线资源分配和用户选择问题被表述为一个优化问题，其目标是在优化 FL 性能的同时最小化 FL 收敛时间。为了解决这个问题，提出了一种概率用户选择方案，使得基站以高概率连接到其局部 FL 模型对其全局 FL 模型有显着影响的用户。给定用户选择策略，可以确定上行链路RB分配。为了进一步减少 FL 收敛时间，