论文标题
通过联合自适应计算和电力控制的空中联合学习
Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control
论文作者
论文摘要
本文考虑了空中联邦学习（OTA-FL）。 OTA-FL利用无线介质的叠加属性，并在空气上免费执行模型聚合。因此，它可以大大降低从边缘设备传达模型更新时产生的通信成本。为了充分利用这一优势，同时提供与常规联合学习的可比学习绩效，该学习假定通过无噪声渠道汇总模型聚合，我们考虑了每个回合的传输量表的关节设计以及在每个边缘设备上的功率约束。我们首先通过建立具有Lipschitz连续梯度的一般函数的基本下限来表征由于OTA-FL中这种通道噪声而导致的训练误差。然后，通过引入自适应收发器功率缩放方案，我们提出了一种使用关节自适应计算和功率控制（ACPC-OTA-FL）的空中联合学习算法。我们在非凸目标函数和异质数据的培训中提供了ACPC-OTA-FL的收敛分析。我们表明，ACPC-OTA-FL的收敛速率与无噪声通信的FL匹配。
This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the superposition property of the wireless medium, and performs model aggregation over the air for free. Thus, it can greatly reduce the communication cost incurred in communicating model updates from the edge devices. In order to fully utilize this advantage while providing comparable learning performance to conventional federated learning that presumes model aggregation via noiseless channels, we consider the joint design of transmission scaling and the number of local iterations at each round, given the power constraint at each edge device. We first characterize the training error due to such channel noise in OTA-FL by establishing a fundamental lower bound for general functions with Lipschitz-continuous gradients. Then, by introducing an adaptive transceiver power scaling scheme, we propose an over-the-air federated learning algorithm with joint adaptive computation and power control (ACPC-OTA-FL). We provide the convergence analysis for ACPC-OTA-FL in training with non-convex objective functions and heterogeneous data. We show that the convergence rate of ACPC-OTA-FL matches that of FL with noise-free communications.
