Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

In recent years, as the boundaries of computing have expanded with the emergence of the
Internet of Things (IoT) and its increasing number of devices continuously producing flows of …

Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated
remarkable success in a wide range of applications, driven by their ability to leverage vast …

With the development of the technologies deployed on vehicles, there is a significant
increase in the amount of data, which comes from various applications, such as battery …

Federated Learning (FL) has emerged as a powerful approach that enables collaborative
distributed model training without the need for data sharing. However, FL grapples with …

Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-
empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to …

Recent advances in machine learning have highlighted Federated Learning (FL) as a
promising approach that enables multiple distributed users (so-called clients) to collectively …

Federated Learning (FL) is extensively used to train AI/ML models in distributed and privacy-
preserving settings. Participant edge devices in FL systems typically contain non …

Federated Learning (FL) has emerged as a promising solution in Edge Computing (EC)
environments to process the proliferation of data generated by edge devices. By …

Federated Learning (FL) is a collaborative machine learning framework that allows multiple
users to train models utilizing their local data in a distributed manner. However …