The sixth-generation (6G) wireless communication is expected to support ubiquitous artificial
intelligence (AI) applications from the network core to the end devices. The computational …

Federated learning (FL) as a promising edge-learning framework can effectively address the
latency and privacy issues by featuring distributed learning at the devices and model …

The rapid growth in storage capacity and computational power of mobile devices is making it
increasingly attractive for devices to process data locally instead of risking privacy by …

Federated learning (FL) has recently emerged as a promising technology to enable artificial
intelligence (AI) at the network edge. To improve the communication efficiency of FL, over …

To exploit massive amounts of data at mobile edge networks, federated learning (FL) has
been proposed as an attractive substitute for centralized machine learning. To improve the …

Federated learning (FL) is a popular privacy-preserving distributed training scheme, where
multiple devices collaborate to train machine learning models by uploading local model …

Federated learning (FL) has been recognized as a viable distributed learning paradigm
which trains a machine learning model collaboratively with massive mobile devices in the …

As a popular distributed machine learning based on stochastic gradient decent (SGD),
federated learning enables edge devices to compute stochastic gradients and then upload …

Federated learning (FL) is a framework for distributed learning of centralized models. In FL,
a set of edge devices train a model using their local data, while repeatedly exchanging their …

A new machine learning (ML) technique termed as federated learning (FL) aims to preserve
data at the edge devices and to only exchange ML model parameters in the learning …