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 …

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 …

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 …

In this paper, a framework based on a sparse Mixture of Experts (MoE) architecture is
proposed for the federated learning and application of a distributed classification model in …

This paper presents a Scalable Vertical Federated Learning (SVFL) framework designed to
address the task of clas-sification in the cybersecurity domain. SVFL combines vertical …