Federated Learning (FL) is a newly emerged decentralized machine learning (ML)
framework that combines on-device local training with server-based model synchronization …

Federated learning becomes increasingly attractive in the areas of wireless communications
and machine learning due to its powerful learning ability and potential applications. In …

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) was designed to enable mobile phones to collaboratively learn a
global model without uploading their private data to a cloud server. However, exiting FL …

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 …

The explosive growth of smart devices (eg, mobile phones, vehicles, drones) with sensing,
communication, and computation capabilities gives rise to an unprecedented amount of …

This paper considers improving wireless communication and computation efficiency in
federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge …

Federated learning is an attractive distributed learning framework where edge server
coordinates edge devices to collaboratively train an artificial intelligence (AI) model while …

Federated learning (FL) has attracted increasing attention as a promising technique to drive
a vast number of edge devices with artificial intelligence. However, it is very challenging to …

Federated learning (FL) can train a global model from clients' local data set, which can make
full use of the computing resources of clients and performs more extensive and efficient …