Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control
Hai‐Bo Yang, Peiwen Qiu, Liu Jia, Aylin Yener
Abstract
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 adjustment (ACPA-OTA-FL). We provide the convergence analysis for ACPA-OTA-FL in training with non-convex objective functions and heterogeneous data. We show that the convergence rate of ACPA-OTA-FL matches that of FL with noise-free communications.