Federated learning (FL) is a promising paradigm for future sixth-generation wireless systems
to underpin network edge intelligence for smart cities applications. However, most of the …

Recent years have witnessed a huge demand for artificial intelligence and machine learning
applications in wireless edge networks to assist individuals with real-time services …

To leverage massive distributed data and computation resources, machine learning in the
network edge is considered to be a promising technique, especially for large-scale model …

Federated Learning (FL) is a state-of-the-art technique used to build machine learning (ML)
models based on distributed data sets. It enables In-Edge AI, preserves data locality …

Federated Learning (FL) is a distributed machine learning technique, where each device
contributes to the learning model by independently computing the gradient based on its …

Federated learning (FL) can train a global model from clients' local dataset, which can make
full use of the computing resources of clients and performs more extensive and efficient …

Federated learning provides a promising paradigm to enable network edge intelligence in
the future sixth generation (6G) systems. However, due to the high dynamics of wireless …

Federated learning (FL) is a distributed machine learning technology for next-generation AI
systems that allows a number of workers, ie, edge devices, collaboratively learn a shared …

Client and Internet of Things devices are increasingly equipped with the ability to sense,
process, and communicate data with high efficiency. This is resulting in a major shift in …

As the proliferation of sophisticated task models in 5G-empowered digital twin, it yields
significant demands on fast and accurate model training over resource-limited wireless …