Federated learning is a promising approach for training models on distributed data, driven
by increasing demand in various industries. However, federated learning framework faces …

Federated Learning (FL) is a distributed model training framework that allows multiple
clients to collaborate on training a global model without disclosing their local data in edge …

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 …

The challenge of personalized federated learning (pFL) is to capture the heterogeneity
properties of data with in-expensive communications and achieving customized …

Data generated at the network edge can be processed locally by leveraging the paradigm of
Edge Computing (EC). Aided by EC, Federated Learning (FL) has been becoming a …

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

Under the federated learning paradigm, a set of nodes can cooperatively train a machine
learning model with the help of a centralized server. Such a server is also tasked with …

Federated learning has received significant attention as a potential solution for distributing
machine learning (ML) model training through edge networks. This work addresses an …

Transferring large models in federated learning (FL) networks is often hindered by clients'
limited bandwidth. We propose, an FL algorithm which achieves fast convergence by …

Federated learning (FL) enables collaborative model training over distributed computing
nodes without sharing their privacy-sensitive raw data. However, in FL, iterative exchanges …