The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

With the emergence of AI regulations, such as the EU AI Act, requirements for simple data
lineage, enforcement of low data bias, and energy efficiency have become a priority for …

Federated Learning (FL) introduces a novel methodology with the potential to achieve
enhanced privacy and security assurances compared to existing methods. This is achieved …

Federated learning (FL) is a distributed machine learning framework where the global model
of a central server is trained via multiple collaborative steps by participating clients without …

The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI
Act) leading the way. Our key inquiry is how this will affect Federated Learning (FL), whose …

In the realm of edge cloud computing (ECC), Federated Learning (FL) revolutionizes the
decentralization of machine learning (ML) models by enabling their training across multiple …

The development of AI applications, especially in large-scale wireless networks, is growing
exponentially, alongside the size and complexity of the architectures used. Particularly …

Federated Learning (FL) is an emerging machine learning technique that enables
distributed model training across data silos or edge devices without data sharing. Yet, FL …

In recent years, Federated Learning (FL) has shown significant advancements in its ability to
perform various natural language processing (NLP) tasks. This work focuses on applying …

Edge intelligence is an emerging paradigm that leverages edge computing to pave the last
mile delivery of artificial intelligence. While pilot efforts on edge intelligence have mostly …