Federated learning (FL) is a machine learning paradigm that targets model training without
gathering the local data dispersed over various data sources. Standard FL, which employs a …

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 …

Federated learning (FL) has been widely recognized as a promising approach by enabling
individual end-devices to cooperatively train a global model without exposing their own …

Federated Learning (FL) is a newly emerged decentralized machine learning (ML)
framework that combines on-device local training with server-based model synchronization …

Federated learning (FL) is a popular framework for training an AI model using distributed
mobile data in a wireless network. It features data parallelism by distributing the learning …

Federated learning (FL) is an attractive paradigm for making use of rich distributed data
while protecting data privacy. Nonetheless, non-ideal communication links and limited …

Federated learning (FL) involves training a model over massive distributed devices, while
keeping the training data localized. This form of collaborative learning exposes new …

Federated learning becomes increasingly attractive in the areas of wireless communications
and machine learning due to its powerful learning ability and potential applications. In …

Federated Learning (FL) is a recently invented distributed machine learning technique that
allows available network clients to perform model training at the edge, rather than sharing it …

Federated learning (FL) was designed to enable mobile phones to collaboratively learn a
global model without uploading their private data to a cloud server. However, exiting FL …