Distributed Learning over a Wireless Network with FSK-Based Majority Vote

TL;DR

This paper introduces a novel FSK-based over-the-air computation scheme for federated edge learning that uses majority voting with signSGD, eliminating the need for channel state information and demonstrating high accuracy in fading channels.

Contribution

It proposes a new FSK-based scheme for distributed learning that simplifies communication and maintains convergence without channel state information.

Findings

High test accuracy in fading channels

Elimination of channel state information requirement

Effective in non-ideal synchronization conditions

Abstract

In this study, we propose an over-the-air computation (AirComp) scheme for federated edge learning (FEEL). The proposed scheme relies on the concept of distributed learning by majority vote (MV) with sign stochastic gradient descend (signSGD). As compared to the state-of-the-art solutions, with the proposed method, edge devices (EDs) transmit the signs of local stochastic gradients by activating one of two orthogonal resources, i.e., orthogonal frequency division multiplexing (OFDM) subcarriers, and the MVs at the edge server (ES) are obtained with non-coherent detectors by exploiting the energy accumulations on the subcarriers. Hence, the proposed scheme eliminates the need for channel state information (CSI) at the EDs and ES. By taking path loss, power control, cell size, and the probabilistic nature of the detected MVs in fading channel into account, we prove the convergence of the distributed learning for a non-convex function. Through simulations, we show that the proposed scheme can provide a high test accuracy in fading channels even when the time-synchronization and the power alignment at the ES are not ideal. We also provide insight into distributed learning for location-dependent data distribution for the MV-based schemes.