Many industry sectors have been pursuing the adoption of Industry 4.0 (I4. 0) ideas and
technologies, which promise to realize lean and just-in-time production through digitization …

In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big
data, fog computing, and edge computing, smart city applications have suffered from issues …

Manufacturers are increasingly aware of the importance of system resilience against
unexpected disruptive occurrences, such as the recent global Covid-19 pandemic and …

Over the past six decades, the computing systems field has experienced significant
transformations, profoundly impacting society with transformational developments, such as …

Federated learning is a privacy-preserving machine learning technique that trains models
across multiple devices holding local data samples without exchanging them. There are …

Nowadays, with the development of artificial intelligence (AI), privacy issues attract wide
attention from society and individuals. It is desirable to make the data available but invisible …

The virtual world created by the development of the Internet, computers, artificial intelligence
(AI), and hardware technologies have brought various degrees of digital transformation to …

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …