Federated learning (FL) has evolved to leverage a distributed dataset from numerous IoT
devices to improve the performance of a Machine Learning (ML) model while preserving the …

Artificial intelligence and machine learning models are key tools in advancing data-driven
healthcare solutions that aim to improve patient care and outcomes. A key step in …

Federated edge learning (FEEL) is a rapidly growing distributed learning technique for next-
generation wireless edge systems. Smart systems across various application domains face …

In this letter, we consider a Coded Edge Computing (CEC) network in which a client
encodes its computation subtasks using the Maximum Distance Separable (MDS) code …

In this article, we study wireless power transfer (WPT) beam scheduling for a system which
consists of IoT devices and a power beacon (PB) using switched beamforming. In such a …

In the process of model training of the federated learning system, how to design an incentive
mechanism to attract more high-quality worker nodes to join is a key issue. The existing …

Federated machine learning (Fed ML) is a new distributed machine learning technique
using clients' local data applied to collaboratively train a global model without transmitting …

Fog computing, which is an extension of cloud computing is one of the cornerstone for
Internet of Things, that witnessed rapid growth because of its ability to enhance several …

Mobile devices are pervasive data producers that bridges users and emerging network
services, eg, learning techniques. Today, mobile devices are continuously generating user …

Federated Learning (FL) is believed as a promising manner of distributed machine learning
for 5G and future 6G networks in the context of mobile edge computing (MEC). From the …