Federated learning (FL) is an attractive paradigm for making use of rich distributed data
while protecting data privacy. Nonetheless, non-ideal communication links and limited …

We focus on over-the-air (OTA) Federated Learning (FL), which has been suggested
recently to reduce the communication overhead of FL due to the repeated transmissions of …

To alleviate the negative impact of noise on wireless federated learning (FL), we propose a
channel-weighted aggregation scheme of FL (CWA-FL), in which the parameter server (PS) …

Federated learning (FL) has been recognized as a promising distributed learning paradigm
to support intelligent applications at the wireless edge, where a global model is trained …

We consider a broadband over-the-air computation empowered model aggregation
approach for wireless federated learning (FL) systems and propose to leverage an …

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 …

Federated Learning (FL) has been proposed as an appealing approach to handle data
privacy issue of mobile devices compared to conventional machine learning at the remote …

To efficiently exploit the massive amounts of raw data that are increasingly being generated
in mobile edge networks, federated learning (FL) has emerged as a promising distributed …

Training task in classical machine learning models, such as deep neural networks, is
generally implemented at a remote cloud center for centralized learning, which is typically …

Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many
edge devices to collaboratively train a global model without sharing their private data. To …