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 emerged as an efficient distributed model training framework
that enables multiple clients cooperatively to train a global model without exposing their …

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 …

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, as one enabling technology of edge intelligence, has gained substantial
attention due to its efficacy in training deep learning models without data privacy and …

Federated learning (FL) is a distributed model for deep learning that integrates client-server
architecture, edge computing, and real-time intelligence. FL has the capability of …

Federated learning (FL) enables edge devices to cooperatively train models without
exposing their raw data. However, implementing a practical FL system at the network edge …

Federated learning (FL) achieves great popularity in broad areas as a powerful interface to
offer intelligent services to customers while maintaining data privacy. Nevertheless, FL faces …

Federated Learning (FL) has achieved significant achievements recently, enabling
collaborative model training on distributed data over edge devices. Iterative gradient or …

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 …