Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing

TL;DR

This paper introduces Over-the-Air Federated Learning (AirFL), a novel approach that combines wireless signal processing with distributed machine learning to improve efficiency and scalability at the network edge.

Contribution

It provides a comprehensive tutorial and classification of AirFL into three design approaches, along with theoretical analysis and practical insights.

Findings

AirFL reduces communication latency and energy consumption.

It offers a unified framework for wireless signal processing in federated learning.

Performance analysis guides future research directions.

Abstract

Over-the-Air Federated Learning (AirFL) is an emerging paradigm that tightly integrates wireless signal processing and distributed machine learning to enable scalable AI at the network edge. By leveraging the superposition property of wireless signals, AirFL performs communication and model aggregation of the learning process simultaneously, significantly reducing latency, bandwidth, and energy consumption. This article offers a tutorial treatment of AirFL, presenting a novel classification into three design approaches: CSIT-aware, blind, and weighted AirFL. We provide a comprehensive guide to theoretical foundations, performance analysis, complexity considerations, practical limitations, and prospective research directions.