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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, underimperfect channel state information (CSI). First, the problem is cast as astochastic optimization problem over a predefined training duration and solvedusing the Lyapunov optimization framework. In order to learn and track thewireless channel, a Gaussian process regression (GPR)-based channel predictionmethod is leveraged and incorporated into the scheduling decision. The proposedscheduling policies are evaluated via numerical simulations, under both perfectand imperfect CSI. Results show that the proposed method reduces the loss ofaccuracy up to 25.8% compared to state-of-the-art client scheduling and RBallocation methods.

Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation