  Federated learning (FL) has been recognized as a viable distributed learning
paradigm which trains a machine learning model collaboratively with massive
mobile devices in the wireless edge while protecting user privacy. Although
various communication schemes have been proposed to expedite the FL process,
most of them have assumed ideal wireless channels which provide reliable and
lossless communication links between the server and mobile clients.
Unfortunately, in practical systems with limited radio resources such as
constraint on the training latency and constraints on the transmission power
and bandwidth, transmission of a large number of model parameters inevitably
suffers from quantization errors (QE) and transmission outage (TO). In this
paper, we consider such non-ideal wireless channels, and carry out the first
analysis showing that the FL convergence can be severely jeopardized by TO and
QE, but intriguingly can be alleviated if the clients have uniform outage
probabilities. These insightful results motivate us to propose a robust FL
scheme, named FedTOE, which performs joint allocation of wireless resources and
quantization bits across the clients to minimize the QE while making the
clients have the same TO probability. Extensive experimental results are
presented to show the superior performance of FedTOE for deep learning-based
classification tasks with transmission latency constraints.
