  The quality of wireless communication will directly affect the performance of
federated learning (FL), so this paper analyze the influence of wireless
communication on FL through symbol error rate (SER). In FL system,
non-orthogonal multiple access (NOMA) can be used as the basic communication
framework to reduce the communication congestion and interference caused by
multiple users, which takes advantage of the superposition characteristics of
wireless channels. The Minimum Mean Square Error (MMSE) based serial
interference cancellation (SIC) technology is used to recover the gradient of
each terminal node one by one at the receiving end. In this paper, the gradient
parameters are quantized into multiple bits to retain more gradient information
to the maximum extent and to improve the tolerance of transmission errors. On
this basis, we designed the SER-based device selection mechanism (SER-DSM) to
ensure that the learning performance is not affected by users with bad
communication conditions, while accommodating as many users as possible to
participate in the learning process, which is inclusive to a certain extent.
The experiments show the influence of multi-bit quantization of gradient on FL
and the necessity and superiority of the proposed SER-based device selection
mechanism.
