  In this paper, we propose a device scheduling scheme for differentially
private over-the-air federated learning (DP-OTA-FL) systems, referred to as
S-DPOTAFL, where the privacy of the participants is guaranteed by channel
noise. In S-DPOTAFL, the gradients are aligned by the alignment coefficient and
aggregated via over-the-air computation (AirComp). The scheme schedules the
devices with better channel conditions in the training to avoid the problem
that the alignment coefficient is limited by the device with the worst channel
condition in the system. We conduct the privacy and convergence analysis to
theoretically demonstrate the impact of device scheduling on privacy protection
and learning performance. To improve the learning accuracy, we formulate an
optimization problem with the goal to minimize the training loss subjecting to
privacy and transmit power constraints. Furthermore, we present the condition
that the S-DPOTAFL performs better than the DP-OTA-FL without considering
device scheduling (NoS-DPOTAFL). The effectiveness of the S-DPOTAFL is
validated through simulations.
