With the increasing data scale in the Industrial Internet of Things, edge computing
coordinated with machine learning is regarded as an effective way to raise the novel latency …

Distributed machine learning (ML) was originally introduced to solve a complex ML problem
in a parallel way for more efficient usage of computation resources. In recent years, such …

In applications of the Internet of Things (IoT), where many devices are connected for a
specific purpose, data is continuously collected, communicated, processed, and stored …

Decentralized dual methods play significant roles in large-scale optimization, which
effectively resolve many constrained optimization problems in machine learning and power …

The rapid spread of the Internet of Things (IoT), with billions of connected devices, has
generated huge amounts of data and asks for decentralized solutions for machine learning …

In this article, we consider a class of decentralized resource allocation problems over
directed networks, where each node only communicates with its in-neighbors and attempts …

Objective: The emergence of the concepts like Big Data, Data Science, Machine Learning
(ML), and the Internet of Things (IoT) in recent years has added the potential of research in …

Support Vector Machine (SVM) is a supervised machine learning model for classification
tasks. Training SVM on a large number of data samples is challenging due to the high …

This paper proposes a cluster-aware supervised learning (CluSL) framework, which
integrates the clustering analysis with supervised learning. The objective of CluSL is to …

This paper extends the paradigm of" mobile edge learning (MEL)" by designing an optimal
task allocation scheme for training a machine learning model in an asynchronous manner …