Channel fading can have a strong impact on the convergence of over-the-air federated edge
learning (OTA-FEEL). This paper develops a new and optimal power control policy to …

Over-the-air federated edge learning (OTA-FEEL) is an efficient distributed machine learning
framework in terms of radio resource requirements. Delicate power control is needed to …

Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient solution for
privacy-preserving distributed learning over wireless networks. Air-FEEL allows" one-shot" …

Federated learning (FL) is a promising technique that enables many edge devices to train a
machine learning model collaboratively in wireless networks. By exploiting the superposition …

To enable communication-efficient federated learning, fast model aggregation can be
designed using over-the-air computation (AirComp). In order to implement a reliable and …

Federated edge learning (FEEL) is a popular framework for model training at an edge server
using data distributed at edge devices (eg, smart-phones and sensors) without …

In this paper, we focus on federated learning (FL) over the air based on analog aggregation
transmission in realistic wireless networks. We first derive a closed-form expression for the …

This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the
superposition property of the wireless medium, and performs model aggregation over the air …

Over-the-air federated learning (OTA-FL) has emerged as an efficient mechanism that
exploits the superposition property of the wireless medium and performs model aggregation …