Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
This is an incremental contribution that provides a tutorial and classification for researchers and practitioners in edge AI and wireless communications.
The paper tackles the challenge of enabling scalable AI at the network edge by introducing Over-the-Air Federated Learning (AirFL), which integrates wireless signal processing and distributed machine learning to reduce latency, bandwidth, and energy consumption through simultaneous communication and model aggregation.
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.