As a distributed machine learning technique, federated learning (FL) requires clients to
collaboratively train a shared model with an edge server without leaking their local data …

Federated learning (FL) is a distributed machine learning method in which edge devices
collaboratively train a unified model without disclosing their private training data to others …

Federated Learning (FL) is a very effective distributed machine learning framework that
enables a large number of devices to jointly train models without sharing raw data. However …

Federated learning (FL) is a fast-developing technique that allows multiple workers to train a
global model based on a distributed dataset. Conventional FL (FedAvg) employs gradient …

In federated learning, heterogeneity in the clients' local datasets and computation speeds
results in large variations in the number of local updates performed by each client in each …

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) has been widely adopted to train machine learning models over
massive data in edge computing. However, machine learning faces critical challenges, eg …

Federated learning (FL) enables collaborative training of a global model using localized
data from multiple devices. However, in resource-constrained mobile edge computing …

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