  Federated learning (FL) is an emerging machine learning technique that
aggregates model attributes from a large number of distributed devices. Several
unique features such as energy saving and privacy preserving make FL a highly
promising learning approach for power-limited and privacy sensitive devices.
Although distributed computing can lower down the information amount that needs
to be uploaded, model updates in FL can still experience performance
bottleneck, especially for updates via wireless connections. In this work, we
investigate the performance of FL update with mobile edge devices that are
connected to the parameter server (PS) with practical wireless links, where
uplink update from user to PS has very limited capacity. Different from the
existing works, we apply non-orthogonal multiple access (NOMA) together with
gradient compression in the wireless uplink. Simulation results show that our
proposed scheme can significantly reduce aggregation latency while achieving
similar accuracy.
