In recent years, Federated Edge Learning has gained interest from both industry and
academia for deployment at the wireless network edge. However, some resource-restricted …

Researchers and practitioners have recently shown interest in deploying federated learning
for enhanced privacy preservation in wireless edge networks. In such settings, resource …

Nowadays, users are becoming more reserved in uploading their own data to the servers of
service providers for fear of personal information disclosure. In order to meet the need on …

In Federated edge learning (FEEL), energy-constrained devices at the network edge
consume significant energy when training and uploading their local machine learning …

Abstract Federated Edge Learning (FEEL) is a promising distributed machine learning
paradigm in the era of edge intelligence, which supports to learn the knowledge in the …

This paper studies a federated edge learning system, in which an edge server coordinates a
set of edge devices to train a shared machine learning (ML) model based on their locally …

Due to the increasing demand from mobile devices for the real-time response of cloud
computing services, federated edge learning (FEL) emerges as a new computing paradigm …

Federated edge learning (FEEL) is a promising distributed learning technique for next-
generation wireless networks. FEEL preserves the user's privacy, reduces the …

In this letter, a novel joint dataset and computation management (DCM) scheme for energy-
efficient federated learning (FL) in mobile edge computing (MEC) is proposed. For this …

In this chapter, we aim to design a dynamic scheduling policy to explore the spectrum
flexibility for heterogeneous fe derated e dge l earning (FEEL), so as to facilitate the …