Federated learning (FL) is a machine learning model that preserves data privacy in the
training process. Specifically, FL brings the model directly to the user equipments (UEs) for …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …

Owing to the increasing need for massive data analysis and model training at the network
edge, as well as the rising concerns about the data privacy, a new distributed training …

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

There is an increasing interest in a fast-growing machine learning technique called
Federated Learning (FL), in which the model training is distributed over mobile user …

Federated learning (FL) involves training machine learning models over distributed edge
nodes (ie, workers) while facing three critical challenges, edge heterogeneity, Non-IID data …

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 …

There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …

Federated learning (FL) is a novel machine learning setting that enables on-device
intelligence via decentralized training and federated optimization. Deep neural networks' …