The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …

Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …

By exploiting the computing power and local data of distributed clients, federated learning
(FL) features ubiquitous properties such as reduction of communication overhead and …

This article proposesa novel federated reinforcement learning (FRL) approach for the
energy management of multiple smart homes with home appliances, a solar photovoltaic …

In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly
train a neural network by communicating learning updates with an access point without …

The paradigm of edge computing has formed an innovative scope within the domain of the
Internet of Things (IoT) through expanding the services of the cloud to the network edge to …

We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …

We consider a federated learning framework in which a parameter server (PS) trains a
global model by using n clients without actually storing the client data centrally at a cloud …

Federated learning (FL) is a promising technique that enables a large amount of edge
computing devices to collaboratively train a global learning model. Due to the …