Motivated by the ever-increasing demands for massive data processing and intelligent data
analysis at the network edge, federated learning (FL), a distributed architecture for machine …

Federated learning (FL) enables clients to collaboratively learn a shared task while keeping
data privacy, which can be adopted at the edge of wireless networks to improve edge …

By exploiting the computing power and local data of distributed clients, federated learning
(FL) features ubiquitous properties such as reduction of communication overhead and …

Owing to the increasing need for massive data analysis and model training at the network
edge, as well as the rising concerns about the data privacy, a new distributed training …

Federated learning (FL) leverages the private data and computing power of multiple clients
to collaboratively train a global model. Many existing FL algorithms over wireless networks …

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

This paper proposes a client selection method for federated learning (FL) when the
computation and communication resource of clients cannot be estimated; the method trains …

Federated Learning (FL) is a distributed machine learning technique, where each device
contributes to the learning model by independently computing the gradient based on its …

Federated learning (FL) is a machine learning model that preserves data privacy in the
training process. Specifically, FL brings the model directly to the user equipments (UEs) for …

To overcome the challenge of limited bandwidth, client selection has been considered an
effective method for optimizing Federated Learning (FL). However, since the volatility of the …