  Consider a device that is connected to an edge processor via a communication
channel. The device holds local data that is to be offloaded to the edge
processor so as to train a machine learning model, e.g., for regression or
classification. Transmission of the data to the learning processor, as well as
training based on Stochastic Gradient Descent (SGD), must be both completed
within a time limit. Assuming that communication and computation can be
pipelined, this letter investigates the optimal choice for the packet payload
size, given the overhead of each data packet transmission and the ratio between
the computation and the communication rates. This amounts to a tradeoff between
bias and variance, since communicating the entire data set first reduces the
bias of the training process but it may not leave sufficient time for learning.
Analytical bounds on the expected optimality gap are derived so as to enable an
effective optimization, which is validated in numerical results.
