论文标题
设备安排与差异隐私的空中联合学习
Device Scheduling for Over-the-Air Federated Learning with Differential Privacy
论文作者
论文摘要
在本文中，我们提出了一种针对差异私人空中联合学习（DP-OTA-FL）系统的设备调度方案，称为S-DPOTAFL，其中参与者的隐私是由Channel Noise保证的。在S-DPOTAFL中，梯度由比对系数对齐，并通过空中计算（AIRCOMP）进行汇总。该方案在训练中安排具有更好的通道条件的设备，以避免对齐系数受系统状况最差的设备的限制。我们对理论上进行隐私和融合分析，以证明设备调度对隐私保护和学习绩效的影响。为了提高学习准确性，我们制定了一个优化问题，目的是最大程度地减少受到隐私和传输功率约束的训练损失。此外，我们介绍了S-DPOTAFL在不考虑设备调度（NOS-DPOTAFL）的情况下的性能要好于DP-OTA-FL。 S-DPOTAFL的有效性通过模拟验证。
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.
