Federated learning (FL) has been promoted as a popular technique for training machine
learning (ML) models over edge/fog networks. Traditional implementations of FL have …

To facilitate the deployment of machine learning in resource and privacy-constrained
systems such as the Internet of Things, federated learning (FL) has been proposed as a …

Federated learning has generated significant interest, with nearly all works focused on a
“star” topology where nodes/devices are each connected to a central server. We migrate …

Federated Learning (FL) is a recently invented distributed machine learning technique that
allows available network clients to perform model training at the edge, rather than sharing it …

Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …

The ultra-low latency requirements of 5G/6G applications and privacy constraints call for
distributed machine learning systems to be deployed at the edge. With its simple yet …

Abstract Recently, Federated Learning (FL) has been explored as a new paradigm that
preserves both data privacy and end-users knowledge while reducing latency during model …

The number of Internet of Things (IoT) applications, especially latency‐sensitive ones, have
been significantly increased. So, cloud computing, as one of the main enablers of the IoT …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

In this article, we propose a fog-enabled federated learning framework-FogFL-to facilitate
distributed learning for delay-sensitive applications in resource-constrained IoT …