To exploit the massive amounts of onboard data in vehicular networks while protecting data
privacy and security, federated learning (FL) is regarded as a promising technology to …

To alleviate the substantial local training burden on clients in the federated learning (FL)
process, this paper proposes a more efficient approach based on hybrid federated and …

With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely
considered for application in wireless networks for distributed model training. However, data …

Federated learning (FL) provides a new paradigm for protecting data privacy by enabling
model training at devices and model aggregation at servers. However, data information may …

Federated Learning (FL) has become a popular decentralized learning paradigm to train a
machine learning model using distributed mobile devices without compromising user …

Wireless federated learning (FL) is a new distributed machine learning framework that trains
a global model through user collaboration over wireless networks. However, the resource …

Guaranteeing precise perception for au-tonomous driving systems in diverse driving
conditions requires continuous improvement and training of the perception models. In …

Electronic healthcare (e-health) allows smart devices and medical institutions to
collaboratively collect patients' data, which is trained by artificial intelligence (AI) …

With the advent of the 5G and 6G eras and the explosive growth of mobile users, machine
learning (ML) is increasingly used for extracting important information from a large amount of …

The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge
Machine Learning (EML), has become a highly regarded research area by utilizing …