Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices
without compromising their privacy. As computing tasks are increasingly performed by a …

Federated learning (FL) is widely regarded as highly promising because it enables the
collaborative training of high-performance machine learning models among a large number …

Federated learning (FL) is a collaborative machine learning paradigm where network-edge
clients train a global model under the orchestration of a central server. Unlike traditional …

At the network edge, federated learning (FL) has gained attention as a promising approach
for training deep learning (DL) models collaboratively across a large number of devices …

Federated Learning (FL) has emerged as a promising solution in Edge Computing (EC)
environments to process the proliferation of data generated by edge devices. By …

Federated learning (FL) enables collaborative model training over distributed computing
nodes without sharing their privacy-sensitive raw data. However, in FL, iterative exchanges …

Federated learning (FL) is widely used in edge environments as a privacy-preserving
collaborative learning paradigm. However, edge devices often have heterogeneous …

Federated Learning (FL) has been a promising paradigm in distributed machine learning
that enables in-situ model training and global model aggregation. While it can well preserve …

Federated learning (FL) enables multiple devices to collaboratively train a shared model
while preserving data privacy. However, despite its emerging applications in many areas …

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of
the world's web traffic, making a great data source for various machine learning (ML) …