  Federated learning (FL) has recently emerged as an attractive decentralized
solution for wireless networks to collaboratively train a shared model while
keeping data localized. As a general approach, existing FL methods tend to
assume perfect knowledge of the Channel State Information (CSI) during the
training phase, which may not be easy to acquire in case of fast fading
channels. Moreover, literature analyses either consider a fixed number of
clients participating in the training of the federated model, or simply assume
that all clients operate at the maximum achievable rate to transmit model data.
In this paper, we fill these gaps by proposing a training process that takes
channel statistics as a bias to minimize the convergence time under imperfect
CSI. Numerical experiments demonstrate that it is possible to reduce the
training time by neglecting model updates from clients that cannot sustain a
minimum predefined transmission rate. We also examine the trade-off between
number of clients involved in the training process and model accuracy as a
function of different fading regimes.
