Distributed machine learning is a mainstream system to learn insights for analytics and
intelligence services of many fronts (eg, health, streaming and business) from their massive …

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

To leverage data and computation capabilities of mobile devices, machine learning
algorithms are deployed at the network edge for training artificial intelligence (AI) models …

Federated learning (FL) has been recognized as a viable distributed learning paradigm
which trains a machine learning model collaboratively with massive mobile devices in the …

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 …

The performance capabilities of models trained in a federated learning (FL) setting over
wireless networks can be significantly affected by the underlying properties of the …

Federated learning has emerged as a popular technique for distributing machine learning
(ML) model training across the wireless edge. In this paper, we propose two timescale …

Mobile Edge Computing (MEC) is considered an essential future service for the
implementation of 5G networks and the Internet of Things, as it is the best method of …

Decentralized learning enables edge users to collaboratively train models by exchanging
information via device-to-device communication, yet prior works have been limited to …

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