Federated learning (FL), a novel distributed machine learning (DML) approach, has been
widely adopted to train deep neural networks (DNNs), over massive data in edge computing …

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 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) has been widely adopted to process the enormous data in the
application scenarios like Edge Computing (EC). However, the commonly-used …

Federated learning (FL) has attracted more and more attention recently. The integration of
FL and edge computing makes the edge system more efficient and intelligent. FL usually …

Federated learning (FL) has been widely used to train machine learning models over
massive data in edge computing. However, the existing FL solutions may cause long …

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 …

Deep learning has obtained a great success in computing technologies and has been
affiliated to people's lives inseparably recent years. Nowadays, the most common approach …

While data is distributed in multiple edge devices, federated learning (FL) is attracting more
and more attention to collaboratively train a machine learning model without transferring raw …

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 …