  A new machine learning (ML) technique termed as federated learning (FL) aims
to preserve data at the edge devices and to only exchange ML model parameters
in the learning process. FL not only reduces the communication needs but also
helps to protect the local privacy. Although FL has these advantages, it can
still experience large communication latency when there are massive edge
devices connected to the central parameter server (PS) and/or millions of model
parameters involved in the learning process. Over-the-air computation (AirComp)
is capable of computing while transmitting data by allowing multiple devices to
send data simultaneously by using analog modulation. To achieve good
performance in FL through AirComp, user scheduling plays a critical role. In
this paper, we investigate and compare different user scheduling policies,
which are based on various criteria such as wireless channel conditions and the
significance of model updates. Receiver beamforming is applied to minimize the
mean-square-error (MSE) of the distortion of function aggregation result via
AirComp. Simulation results show that scheduling based on the significance of
model updates has smaller fluctuations in the training process while scheduling
based on channel condition has the advantage on energy efficiency.
