  As it has been discussed in the first part of this work, the utilization of
advanced multiple access protocols and the joint optimization of the
communication and computing resources can facilitate the reduction of delay for
wireless federated learning (WFL), which is of paramount importance for the
efficient integration of WFL in the sixth generation of wireless networks (6G).
To this end, in this second part we introduce and optimize a novel
communication protocol for WFL networks, that is based on non-orthogonal
multiple access (NOMA). More specifically, the Compute-then-Transmit NOMA
(CT-NOMA) protocol is introduced, where users terminate concurrently the local
model training and then simultaneously transmit the trained parameters to the
central server. Moreover, two different detection schemes for the mitigation of
inter-user interference in NOMA are considered and evaluated, which correspond
to fixed and variable decoding order during the successive interference
cancellation process. Furthermore, the computation and communication resources
are jointly optimized for both considered schemes, with the aim to minimize the
total delay during a WFL communication round. Finally, the simulation results
verify the effectiveness of CT-NOMA in terms of delay reduction, compared to
the considered benchmark that is based on time-division multiple access.
