  In the Internet of Things, learning is one of most prominent tasks. In this
paper, we consider an Internet of Things scenario where federated learning is
used with simultaneous transmission of model data and wireless power. We
investigate the trade-off between the number of communication rounds and
communication round time while harvesting energy to compensate the energy
expenditure. We formulate and solve an optimization problem by considering the
number of local iterations on devices, the time to transmit-receive the model
updates, and to harvest sufficient energy. Numerical results indicate that
maximum ratio transmission and zero-forcing beamforming for the optimization of
the local iterations on devices substantially boost the test accuracy of the
learning task. Moreover, maximum ratio transmission instead of zero-forcing
provides the best test accuracy and communication round time trade-off for
various energy harvesting percentages. Thus, it is possible to learn a model
quickly with few communication rounds without depleting the battery.
