In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

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 rapidly growing research field in machine learning. However,
existing FL libraries cannot adequately support diverse algorithmic development; …

Federated Learning allows training machine learning models by using the computation and
private data resources of many distributed clients such as smartphones and IoT devices …

Federated learning promises to use the computational power of edge devices while
maintaining user data privacy. Current frameworks, however, typically make the unrealistic …

Federated Semi-Supervised Learning (FedSSL) has gained rising attention from both
academic and industrial researchers, due to its unique characteristics of co-training machine …

Deploying machine learning applications on edge devices can bring clear benefits such as
improved reliability, latency and privacy but it also introduces its own set of challenges. Most …

Many existing federated learning (FL) algorithms are designed for supervised learning tasks,
assuming that the local data owned by the clients are well labeled. However, in many …

Federated Learning has shown great potentials for the distributed data utilization and
privacy protection. Most existing federated learning approaches focus on the supervised …

The rapid growth of the telecommunication industry presents a global challenge in
maintaining data security and privacy amid increasing data traffic and diverse applications …