Federated learning (FL) is a distributed learning paradigm that enables a large number of
mobile devices to collaboratively learn a model under the coordination of a central server …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
devices to collaboratively learn a model without sharing their raw data. Despite its practical …

Federated learning (FL) is promising in enabling large-scale model training by massive
devices without exposing their local datasets. However, due to limited wireless resources …

Federated learning has emerged recently as a promising solution for distributing machine
learning tasks through modern networks of mobile devices. Recent studies have obtained …

Federated learning (FL) has recently emerged as an important and promising learning
scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets …

Federated learning can achieve distributed machine learning without sharing privacy and
sensitive data of end devices. However, high concurrent access to cloud servers increases …

The coupling of federated learning (FL) and multi-access edge computing (MEC) has the
potential to foster numerous applications. However, it poses great challenges to train FL fast …

Training task in classical machine learning models, such as deep neural networks, is
generally implemented at a remote cloud center for centralized learning, which is typically …

Federated learning is a communication-efficient training process that alternate between
local training at the edge devices and averaging of the updated local model at the center …

Federated learning (FL) is an emerging paradigm to train a global deep neural network
(DNN) model by collaborative clients that store their private data locally through the …