  In this work, we propose a novel joint client scheduling and resource block
(RB) allocation policy to minimize the loss of accuracy in federated learning
(FL) over wireless compared to a centralized training-based solution, under
imperfect channel state information (CSI). First, the problem is cast as a
stochastic optimization problem over a predefined training duration and solved
using the Lyapunov optimization framework. In order to learn and track the
wireless channel, a Gaussian process regression (GPR)-based channel prediction
method is leveraged and incorporated into the scheduling decision. The proposed
scheduling policies are evaluated via numerical simulations, under both perfect
and imperfect CSI. Results show that the proposed method reduces the loss of
accuracy up to 25.8% compared to state-of-the-art client scheduling and RB
allocation methods.
