Federated learning (FL) has been recognized as a viable distributed learning paradigm
which trains a machine learning model collaboratively with massive mobile devices in the …

Communication has been known to be one of the primary bottlenecks of federated learning
(FL), and yet existing studies have not addressed the efficient communication design …

Federated learning (FL) is a powerful distributed machine learning framework where a
server aggregates models trained by different clients without accessing their private data …

Traditional deep learning models are trained at a centralized server using data samples
collected from users. Such data samples often include private information, which the users …

Federated learning (FL) over mobile devices has fostered numerous intriguing
applications/services, many of which are delay-sensitive. In this paper, we propose a service …

Federated learning (FL) has attracted tremendous attentions in recent years due to its
privacy-preserving measures and great potential in some distributed but privacy-sensitive …

There is an increasing interest in a fast-growing machine learning technique called
Federated Learning (FL), in which the model training is distributed over mobile user …

There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …

Traditional deep learning models are trained on centralized servers using labeled sample
data collected from edge devices. This data often includes private information, which the …

Due to the dynamics of wireless channels and limited wireless resources (ie, spectrum),
deploying federated learning (FL) over wireless networks is challenged by frequent FL …