Federated Edge Learning (FEEL) is a distributed machine learning technique where each
device contributes to training a global inference model by independently performing local …

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation
in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel …

In this paper, we propose a framework where over-the-air computation (OAC) occurs in both
uplink (UL) and downlink (DL), sequentially, in a multi-cell environment to address the …

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model
from distributed datasets based on wireless communications. This paper proposes a unit …

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for
distributed machine learning using training data distributed at edge devices. This framework …

Federated learning is a promising approach for training models on distributed data, driven
by increasing demand in various industries. However, federated learning framework faces …

Federated learning (FL) has been widely adopted to train machine learning models over
massive data in edge computing. However, machine learning faces critical challenges, eg …

Machine learning and wireless communication technologies are jointly facilitating an
intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …

Federated edge learning is a promising technology to deploy intelligence at the edge of
wireless networks in a privacy-preserving manner. Under such a setting, multiple clients …

Federated learning (FL) involves training machine learning models over distributed edge
nodes (ie, workers) while facing three critical challenges, edge heterogeneity, Non-IID data …