Federated learning (FL) is an edge learning framework that has received significant
attention recently. However, the cost of communication has become a major challenge for FL …

Federated edge learning is envisioned as the bedrock of enabling intelligence in next-
generation wireless networks, but the limited spectral resources often constrain its …

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

To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

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

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 …

The rapid growth in storage capacity and computational power of mobile devices is making it
increasingly attractive for devices to process data locally instead of risking privacy by …

This paper presents the first broadband digital over-the-air computation (AirComp) system
for phase asynchronous OFDM-based federated edge learning systems. Existing analog …

Federated learning (FL) is a popular privacy-preserving distributed training scheme, where
multiple devices collaborate to train machine learning models by uploading local model …

The popularity of mobile devices results in the availability of enormous data and
computational resources at the network edge. To leverage the data and resources, a new …