The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

Healthcare is predominantly regarded as a crucial consideration in promoting the general
physical and mental health and well‐being of people around the world. The amount of data …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

Federated learning (FL) is a new technology that has been a hot research topic. It enables
the training of an algorithm across multiple decentralized edge devices or servers holding …

Federated learning (FL) is an approach within the realm of machine learning (ML) that
allows the use of distributed data without compromising personal privacy. In FL, it becomes …

User modeling aims to capture the latent characteristics of users from their behaviors, and is
widely applied in numerous applications. Usually, centralized user modeling suffers from the …

Development of Artificial Intelligence (AI) is inherently tied to the development of data.
However, in most industries data exists in form of isolated islands, with limited scope of …

Fog computing is an energy-efficient and cost-effective paradigm to help alleviate the
pressure of resource-constrained mobile devices (MDs) running computation-intensive …

In terms of artificial intelligence, there are several security and privacy deficiencies in the
traditional centralized training methods of machine learning models by a server. To address …