User Scheduling for Federated Learning Through Over-the-Air Computation

TL;DR

This paper explores user scheduling strategies in federated learning using over-the-air computation, aiming to optimize communication efficiency and model update accuracy amid large device networks.

Contribution

It compares different user scheduling policies based on channel conditions and model update significance, incorporating receiver beamforming to reduce aggregation error.

Findings

Scheduling based on model update significance reduces training fluctuations.

Channel condition-based scheduling improves energy efficiency.

Receiver beamforming minimizes mean-square-error in data aggregation.

Abstract

A new machine learning (ML) technique termed as federated learning (FL) aims to preserve data at the edge devices and to only exchange ML model parameters in the learning process. FL not only reduces the communication needs but also helps to protect the local privacy. Although FL has these advantages, it can still experience large communication latency when there are massive edge devices connected to the central parameter server (PS) and/or millions of model parameters involved in the learning process. Over-the-air computation (AirComp) is capable of computing while transmitting data by allowing multiple devices to send data simultaneously by using analog modulation. To achieve good performance in FL through AirComp, user scheduling plays a critical role. In this paper, we investigate and compare different user scheduling policies, which are based on various criteria such as wireless channel conditions and the significance of model updates. Receiver beamforming is applied to minimize the mean-square-error (MSE) of the distortion of function aggregation result via AirComp. Simulation results show that scheduling based on the significance of model updates has smaller fluctuations in the training process while scheduling based on channel condition has the advantage on energy efficiency.