Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
Information Theory
2025-12-04 v1 Artificial Intelligence
Machine Learning
math.IT
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.
Keywords
Cite
@article{arxiv.2512.03719,
title = {Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing},
author = {Seyed Mohammad Azimi-Abarghouyi and Carlo Fischione and Kaibin Huang},
journal= {arXiv preprint arXiv:2512.03719},
year = {2025}
}