  In this paper, we consider a federated learning problem over wireless channel
that takes into account the coding rate and packet transmission errors.
Communication channels are modelled as packet erasure channels (PEC), where the
erasure probability is determined by the block length, code rate, and
signal-to-noise ratio (SNR). To lessen the effect of packet erasure on the FL
performance, we propose two schemes in which the central node (CN) reuses
either the past local updates or the previous global parameters in case of
packet erasure. We investigate the impact of coding rate on the convergence of
federated learning (FL) for both short packet and long packet communications
considering erroneous transmissions. Our simulation results shows that even one
unit of memory has considerable impact on the performance of FL in erroneous
communication.
