  In federated learning (FL), distributed clients can collaboratively train a
shared global model while retaining their own training data locally.
Nevertheless, the performance of FL is often limited by the slow convergence
due to poor communications links when FL is deployed over wireless networks.
Due to the scarceness of radio resources, it is crucial to select clients
precisely and allocate communication resource accurately for enhancing FL
performance. To address these challenges, in this paper, a joint optimization
problem of client selection and resource allocation is formulated, aiming to
minimize the total time consumption of each round in FL over a non-orthogonal
multiple access (NOMA) enabled wireless network. Specifically, considering the
staleness of the local FL models, we propose an age of update (AoU) based novel
client selection scheme. Subsequently, the closed-form expressions for resource
allocation are derived by monotonicity analysis and dual decomposition method.
In addition, a server-side artificial neural network (ANN) is proposed to
predict the FL models of clients who are not selected at each round to further
improve FL performance. Finally, extensive simulation results demonstrate the
superior performance of the proposed schemes over FL performance, average AoU
and total time consumption.
