In this paper, we consider federated learning (FL) over a noisy fading multiple access
channel (MAC), where an edge server aggregates the local models transmitted by multiple …

Federated learning (FL) is an emerging machine learning paradigm that allows to
accomplish model training without aggregating data at a central server. Most studies on FL …

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 …

There is an increasing interest in a fast-growing machine learning technique called
Federated Learning (FL), in which the model training is distributed over mobile user …

Federated learning (FL) is a privacy-preserving distributed machine learning technique that
trains models while keeping all the original data generated on devices locally. Since devices …

Federated learning (FL) involves training a model over massive distributed devices, while
keeping the training data localized. This form of collaborative learning exposes new …

Owing to the increasing need for massive data analysis and model training at the network
edge, as well as the rising concerns about the data privacy, a new distributed training …

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" …

Future wireless networks are expected to support diverse mobile services, including artificial
intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a …

Federated learning (FL) is an efficient and privacy-preserving distributed learning paradigm
that enables massive edge devices to train machine learning models collaboratively …