论文标题
梯度统计信息通知的电力控制
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning
论文作者
论文摘要
联合学习（FL）是一种有前途的技术，它使许多边缘设备能够在无线网络中协作训练机器学习模型。通过利用无线波形的叠加性质，过电计算（AIRCOMP）可以加速模型聚集，从而促进沟通效率高效的FL。由于通道褪色，功率控制对于AirComp至关重要。先前的工作假设要从每个设备汇总的信号，即本地梯度具有相同的统计信息。然而，在FL中，梯度统计数据在训练迭代和特征维度方面有所不同，并且事先未知。本文通过考虑梯度统计数据来研究空运的电力控制问题。目标是通过优化受峰值功率约束的每个设备的发射功率来最大程度地减少聚合误差。当给出梯度统计时，我们以封闭形式获得最佳策略。值得注意的是，我们表明，随着梯度向量的平方多元变化（SMCV），最佳发射功率是连续的，单调降低。然后，我们提出了一种以微不足道的通信成本来估计梯度统计数据的方法。实验结果表明，与现有方案相比，所提出的梯度统计 - 感知功率控制的测试准确性更高。
Federated learning (FL) is a promising technique that enables many edge devices to train a machine learning model collaboratively in wireless networks. By exploiting the superposition nature of wireless waveforms, over-the-air computation (AirComp) can accelerate model aggregation and hence facilitate communication-efficient FL. Due to channel fading, power control is crucial in AirComp. Prior works assume that the signals to be aggregated from each device, i.e., local gradients have identical statistics. In FL, however, gradient statistics vary over both training iterations and feature dimensions, and are unknown in advance. This paper studies the power control problem for over-the-air FL by taking gradient statistics into account. The goal is to minimize the aggregation error by optimizing the transmit power at each device subject to peak power constraints. We obtain the optimal policy in closed form when gradient statistics are given. Notably, we show that the optimal transmit power is continuous and monotonically decreases with the squared multivariate coefficient of variation (SMCV) of gradient vectors. We then propose a method to estimate gradient statistics with negligible communication cost. Experimental results demonstrate that the proposed gradient-statistics-aware power control achieves higher test accuracy than the existing schemes for a wide range of scenarios.
