Federated learning is a model-level aggregation learning paradigm, which can generate
high quality models without collecting the local private data of users. As a distributed …

To provide sustainable energy support while meeting the demands for serving a rapidly
increasing number of devices, in this paper, we propose a new Reconfigurable intelligent …

Motivated by increasing computational capabilities of wireless devices, as well as
unprecedented levels of user-and device-generated data, new distributed machine learning …

Federated learning (FL) in a mobile edge network faces challenges from both
communication and learning per-spectives. The typically non-iid data can lead to slow …

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 …

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 …

Federated Learning (FL) has been widely used to train shared machine learning models
while addressing the privacy concerns. When deployed in wireless networks, bandwidth …

This work studies the task of device coordination in wireless networks for over-the-air
federated learning (OTA-FL). For conventional metrics of aggregation error, the task is …

We study federated learning (FL) with over-the-air aggregation over time-varying wireless
channels. Independent workers compute local gradients based on their local datasets and …

Over-the-air federated learning (OTA-FL) exploits the inherent superposition property of
wireless channels to integrate the communication and model aggregation. Though a …