Traditionally, Artificial Intelligence (AI) models are trained on the central cloud with data
collected from end devices. This leads to high communication cost, long response time, and …

Federated edge learning (FEEL) has emerged as an effective approach to reduce the large
communication latency in Cloud-based machine learning solutions, while preserving data …

The increasing data produced by IoT devices and the need to harness intelligence in our
environments impose the shift of computing and intelligence at the edge, leading to a novel …

A central question in edge intelligence is “how can an edge device learn its local model with
limited data and constrained computing capacity?” In this study, we explore the approach …

Federated Learning (FL) has emerged as an efficient distributed model training framework
that enables multiple clients cooperatively to train a global model without exposing their …

Recently, edge AI has been launched to mine and discover valuable knowledge at network
edge. Federated Learning, as an emerging technique for edge AI, has been widely …

The advancement of the Internet of Things (IoT) brings new opportunities for collecting real-
time data and deploying machine learning models. Nonetheless, an individual IoT device …

Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the
training and inference of machine learning models to the edge of the network. This paradigm …

Federated learning (FL) has been widely adopted to train machine learning models over
massive data in edge computing. However, machine learning faces critical challenges, eg …

Federated learning (FL) constitutes a potent machine learning paradigm extensively applied
in edge computing for training models on vast datasets. However, the challenges of data …