Device Scheduling for Over-the-Air Federated Learning with Differential Privacy

TL;DR

This paper introduces a device scheduling scheme for over-the-air federated learning that enhances privacy and learning accuracy by selecting devices with better channel conditions, supported by theoretical analysis and simulations.

Contribution

The paper proposes a novel device scheduling scheme for differentially private over-the-air federated learning, improving privacy and accuracy by optimizing device selection based on channel conditions.

Findings

Device scheduling improves privacy guarantees.

Scheduling enhances learning accuracy and convergence.

The proposed scheme outperforms non-scheduling methods in simulations.

Abstract

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.