The stringent requirements for low-latency and privacy of the emerging high-stake
applications with intelligent devices such as drones and smart vehicles make the cloud …

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly
prominent isolated data islands problem while keeping users' data locally with privacy and …

The rapid growth in storage capacity and computational power of mobile devices is making it
increasingly attractive for devices to process data locally instead of risking privacy by …

Federated learning (FL) as a promising edge-learning framework can effectively address the
latency and privacy issues by featuring distributed learning at the devices and model …

To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

Federated learning (FL) is a popular framework for training an AI model using distributed
mobile data in a wireless network. It features data parallelism by distributing the learning …

Federated learning (FL) has received considerable attention with the development of mobile
internet technology, which is an emerging framework to train a deep learning model from …

Federated Learning (FL) has been proposed as an appealing approach to handle data
privacy issue of mobile devices compared to conventional machine learning at the remote …

In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

Federated Learning is a collaborative machine learning framework to train a deep learning
model without accessing clients' private data. Previous works assume one central parameter …