Scalable and Reliable Over-the-Air Federated Edge Learning

TL;DR

This paper introduces a scalable digital lattice-based coding scheme for over-the-air federated edge learning that maintains constant error correction capabilities regardless of the number of clients, improving reliability in communication.

Contribution

It proposes a novel lattice-based coding approach for AirComp in FEEL that offers consistent error correction as client numbers grow, outperforming existing nested-lattice codes.

Findings

Constant error correction with increasing clients

Improved reliability over existing coding schemes

Enhanced communication efficiency in FEEL

Abstract

Federated edge learning (FEEL) has emerged as a core paradigm for large-scale optimization. However, FEEL still suffers from a communication bottleneck due to the transmission of high-dimensional model updates from the clients to the federator. Over-the-air computation (AirComp) leverages the additive property of multiple-access channels by aggregating the clients' updates over the channel to save communication resources. While analog uncoded transmission can benefit from the increased signal-to-noise ratio (SNR) due to the simultaneous transmission of many clients, potential errors may severely harm the learning process for small SNRs. To alleviate this problem, channel coding approaches were recently proposed for AirComp in FEEL. However, their error-correction capability degrades with an increasing number of clients. We propose a digital lattice-based code construction with constant error-correction capabilities in the number of clients, and compare to nested-lattice codes, well-known for their optimal rate and power efficiency in the point-to-point AWGN channel.