Federated Learning (FL) enables large-scale machine learning without uploading the
private data of wireless devices. Due to the heterogeneity and limitation of the devices' …

Federated Learning (FL) has been widely used to train shared machine learning models
while addressing the privacy concerns. When deployed in wireless networks, bandwidth …

Federated learning (FL) is a decentralized algorithm that can train a globally shared model
without the requirement to send the raw data to a centralized server by user equipments …

Recently, federated learning (FL) has become a promising distributed learning technology
by collaboratively training shared learning models on clients. However, due to the statistical …

Federated Learning (FL) is widely regarded as a leading distributed machine learning
paradigm, owing to its outstanding performance in preserving privacy and conserving …

Federated learning (FL) is promising in enabling large-scale model training by massive
devices without exposing their local datasets. However, due to limited wireless resources …

Federated Learning (FL) is a promising paradigm for massive data mining service while
protecting users' privacy. In wireless federated learning networks (WFLNs), limited …

Federated learning (FL) is increasingly considered to circumvent the disclosure of private
data in mobile edge computing (MEC) systems. Training with large data can enhance FL …

Federated learning (FL) is a distributed machine learning (ML). Distributed clients train
locally and exclusively need to upload the model parameters to learn the global model …

In recent years, wireless federated learning (FL) has been proposed to support the mobile
intelligent applications over the wireless network, which protects the data privacy and …