The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving
fast model aggregation by exploiting the waveform superposition property of multiple-access …

Federated Learning (FL) refers to distributed protocols that avoid direct raw data exchange
among the participating devices while training for a common learning task. This way, FL can …

Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …

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

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Federated learning (FL) is a promising technique that enables many edge devices to train a
machine learning model collaboratively in wireless networks. By exploiting the superposition …

With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …

Recently, over-the-air computation (AirComp) has emerged as an efficient solution for
access points (APs) to aggregate distributed data from many edge devices (eg, sensors) by …