Unsourced Massive Access-Based Digital Over-the-Air Computation for Efficient Federated Edge Learning

TL;DR

This paper introduces an unsourced massive access-based digital over-the-air computation scheme for federated edge learning, improving convergence speed and efficiency over existing one-bit digital aggregation methods by utilizing shared codebooks and advanced detection algorithms.

Contribution

It proposes a novel unsourced massive access-based digital OAC scheme with shared codebooks and an approximate message passing algorithm, enhancing FEEL convergence and resource efficiency.

Findings

Significantly accelerates FEEL convergence compared to OBDA.

Uses shared codebooks for efficient model update transmission.

Employs approximate message passing for effective detection.

Abstract

Over-the-air computation (OAC) is a promising technique to achieve fast model aggregation across multiple devices in federated edge learning (FEEL). In addition to the analog schemes, one-bit digital aggregation (OBDA) scheme was proposed to adapt OAC to modern digital wireless systems. However, one-bit quantization in OBDA can result in a serious information loss and slower convergence of FEEL. To overcome this limitation, this paper proposes an unsourced massive access (UMA)-based generalized digital OAC (GD-OAC) scheme. Specifically, at the transmitter, all the devices share the same non-orthogonal UMA codebook for uplink transmission. The local model update of each device is quantized based on the same quantization codebook. Then, each device transmits a sequence selected from the UMA codebook based on the quantized elements of its model update. At the receiver, we propose an approximate message passing-based algorithm for efficient UMA detection and model aggregation. Simulation results show that the proposed GD-OAC scheme significantly accelerates the FEEL convergences compared with the state-of-the-art OBDA scheme while using the same uplink communication resources.