Recently, edge AI has been launched to mine and discover valuable knowledge at network
edge. Federated Learning, as an emerging technique for edge AI, has been widely …

Federated learning (FL) has been widely adopted to train machine learning models over
massive distributed data sources in edge computing. However, the existing FL frameworks …

Data generated at the network edge can be processed locally by leveraging the emerging
technology of Federated Learning (FL). However, non-IID local data will lead to degradation …

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

At the network edge, federated learning (FL) has gained attention as a promising approach
for training deep learning (DL) models collaboratively across a large number of devices …

Traditional Federated Learning (FL) is a promising paradigm that enables massive edge
clients to collaboratively train deep neural network (DNN) models without exposing raw data …

In edge computing (EC), federated learning (FL) enables massive devices to collaboratively
train AI models without exposing local data. In order to avoid the possible bottleneck of the …

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) is an emerging approach in edge computing for collaboratively
training machine learning models among multiple devices, which aims to address limited …

Federated learning (FL) is a promising paradigm that enables collaboratively learning a
shared model across massive clients while keeping the training data locally. However, for …