  In this paper, we study communication-efficient distributed stochastic
gradient descent (SGD) with data sets of users distributed over a certain area
and communicating through wireless channels. Since the time for one iteration
in the proposed approach is independent of the number of users, it is
well-suited to scalable distributed SGD. Furthermore, since the proposed
approach is based on preamble-based random access, which is widely adopted for
machine-type communication (MTC), it can be easily employed for training models
with a large number of devices in various Internet-of-Things (IoT) applications
where MTC is used for their connectivity. For fading channel, we show that
noncoherent combining can be used. As a result, no channel state information
(CSI) estimation is required. From analysis and simulation results, we can
confirm that the proposed approach is not only scalable, but also provides
improved performance as the number of devices increases.
