As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …

The individual and integration use of the Internet of Things (IoT), Information-Centric
Networking (ICN), and Federated Learning (FL) have recently been used in several network …

Digital Twin (DT) technology has experienced substantial advancements and extensive
adoption across various industries, aiming to enhance operational efficiency and …

Semantic segmentation is a promising machine learning (ML) method for highly precise fine-
scale defect detection and part qualification in additive manufacturing (AM). Most existing …

The constantly evolving digital transformation imposes new requirements on our society.
Aspects relating to reliance on the networking domain and the difficulty of achieving security …

Rotating machinery is ubiquitous in modern industrial systems. Ensuring optimal operating
conditions for rotating machinery is essential to satisfy stringent requirements on safety …

In this paper, we propose GIFAIR-FL, a framework that imposes group and individual
fairness (GIFAIR) to federated learning (FL) settings. By adding a regularization term, our …

The increase in the computational power of edge devices has opened up the possibility of
processing some of the data at the edge and distributing model learning. This paradigm is …

A vast amount of data is created every minute, both in the private sector and industry.
Whereas it is often easy to get hold of data in the private entertainment sector, in the …

Over the years, Internet of Things (IoT) devices have become more powerful. This sets forth
a unique opportunity to exploit local computing resources to distribute model learning and …