Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning

TL;DR

This paper introduces a novel digital over-the-air computation scheme for federated edge learning that encodes local gradients using balanced number systems, enabling efficient aggregation without precise synchronization or channel estimation.

Contribution

It proposes a new OAC scheme based on balanced number systems, eliminating the need for synchronization and channel inversion, and analyzes its performance and convergence in FEEL.

Findings

Achieves up to 98% test accuracy with AAM in heterogeneous data.

Provides theoretical analysis of MSE and convergence rate.

Demonstrates effective gradient aggregation without channel estimation.

Abstract

In this study, we propose a digital over-the-air computation (OAC) scheme for achieving continuous-valued (analog) aggregation for federated edge learning (FEEL). We show that the average of a set of real-valued parameters can be calculated approximately by using the average of the corresponding numerals, where the numerals are obtained based on a balanced number system. By exploiting this key property, the proposed scheme encodes the local stochastic gradients into a set of numerals. Next, it determines the positions of the activated orthogonal frequency division multiplexing (OFDM) subcarriers by using the values of the numerals. To eliminate the need for precise sample-level time synchronization, channel estimation overhead, and channel inversion, the proposed scheme also uses a non-coherent receiver at the edge server (ES) and does not utilize a pre-equalization at the edge devices (EDs). We theoretically analyze the MSE performance of the proposed scheme and the convergence rate for a non-convex loss function. To improve the test accuracy of FEEL with the proposed scheme, we introduce the concept of adaptive absolute maximum (AAM). Our numerical results show that when the proposed scheme is used with AAM for FEEL, the test accuracy can reach up to 98% for heterogeneous data distribution.