Federated learning (FL) is a promising paradigm that enables distributed clients to collaboratively train a shared global model while keeping the training data locally. However, the performance of FL is often limited by poor communication links and slow convergence when FL is deployed over wireless networks. Besides, due to the limited radio resources, it is crucial to select clients and control resource allocation accurately for improved FL performance. Motivated by these challenges, a joint optimization problem of client selection and resource allocation is formulated in this paper, aiming to minimize the total time consumption of each round in FL over non-orthogonal multiple access (NOMA) enabled wireless network. Specifically, based on a metric termed the age of update (AoU), we first propose a novel client selection scheme by accounting for the staleness of the received local FL models. After that, the closed-form solutions of resource allocation are obtained by monotonicity analysis and dual decomposition method. Moreover, to further improve the performance of FL, the deployment of artificial neural network (ANN) at the server is proposed to predict the local FL models of the unselected clients at each round. Finally, extensive simulation results demonstrate the superior performance of the proposed schemes.

翻译：联邦学习是一种有前景的范式，它使分布式客户端能够在本地保留训练数据的同时，协作训练共享的全局模型。然而，当联邦学习部署在无线网络上时，其性能常因通信链路质量差和收敛缓慢而受限。此外，由于无线资源有限，精确选择客户端并控制资源分配对于提升联邦学习性能至关重要。受这些挑战的启发，本文提出了一种联合优化客户端选择与资源分配的问题，旨在最小化非正交多址无线网络联邦学习每轮的总时间消耗。具体而言，基于一种称为更新时效性的度量，我们首先提出了一种新颖的客户端选择方案，该方案考虑了接收到的本地联邦模型的陈旧性。随后，通过单调性分析和对偶分解方法，得到了资源分配的闭式解。此外，为进一步提升联邦学习性能，提出在服务器端部署人工神经网络来预测每轮未选中客户端的本地联邦模型。最后，大量仿真结果表明了所提方案的优越性能。