Communication and computation are often viewed as separate tasks. This approach is very
effective from the perspective of engineering as isolated optimizations can be performed …

In order to meet the extremely heterogeneous requirements of the next generation wireless
communication networks, research community is increasingly dependent on using machine …

Current sound-based practices and systems developed in both academia and industry point
to convergent research trends that bring together the field of sound and music Computing …

The aim of this paper is to propose a resource allocation strategy for dynamic training and
inference of machine learning tasks at the edge of the wireless network, with the goal of …

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 …

Abstract The Tactile Internet of Things (TIoT) represents a special class of the Internet of
Things (IoT) that has opened the door for a new generation of agile, highly dynamic …

The Internet of Things is rapidly growing with the demand for low-power, long-range
wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one …

Machine Learning (ML) algorithms process input data making it possible to recognize and
extract patterns from a large data volume. Likewise, Internet of Things (IoT) devices provide …

In this study, we propose an over-the-air computation (AirComp) scheme for federated edge
learning (FEEL). The proposed scheme relies on the concept of distributed learning by …

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