In this work, a Hybrid Hierarchical Federated Edge Learning (HHFEL) architecture that
consists of a device layer, an edge layer, and a cloud layer over heterogeneous networks, is …

Federated Edge Learning (FEL) enables a massive number of edge devices (eg smart
phones) to train machine learning models collaboratively. Due to the inherent unreliability of …

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 …

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

Federated learning (FL) enables collaborative model training without centralizing data.
However, the traditional FL framework is cloud-based and suffers from high communication …

To cope with the high communication overhead caused by frequent aggregation of
Federated Learning (FL) in Multi-access Edge Computing (MEC) scenarios, Hierarchical …

Low latency, resource efficiency, and data privacy are some of the crucial requirements in
modern communication networks. Federated learning can efficiently address these issues …

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 …

When applying machine learning techniques to the Internet of things, aggregating massive
amount of data seriously reduce the system efficiency. To tackle this challenge, a distributed …

Federated learning (FL) involves training machine learning models over distributed edge
nodes (ie, workers) while facing three critical challenges, edge heterogeneity, Non-IID data …