The concept of hierarchical federated edge learning (H-FEEL) has been recently proposed
as an enhancement of federated learning model. Such a system generally consists of three …

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 …

Federated learning (FL) is promising in enabling large-scale model training by massive
devices without exposing their local datasets. However, due to limited wireless resources …

Efficient collaboration between collaborative machine learning and wireless communication
technology, forming a Federated Edge Learning (FEEL), has spawned a series of next …

Emerging technologies and applications including Internet of Things, social networking, and
crowd-sourcing generate large amounts of data at the network edge. Machine learning …

Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It
enables distributed learning to train on cross-device data, achieving efficient performance …

The combination of artificial intelligence and mobile edge computing (MEC) is considered as
a promising evolution path of the future wireless networks. As a model-level coordination …

In this article, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In the considered model, wireless users execute an FL …

Federated Learning is a new learning scheme for collaborative training a shared prediction
model while keeping data locally on participating devices. In this paper, we study a new …

Federated learning can achieve distributed machine learning without sharing privacy and
sensitive data of end devices. However, high concurrent access to cloud servers increases …