Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

Federated learning (FL) has emerged as a promising paradigm for collaborative model
training while preserving data privacy. Client selection plays a crucial role in determining the …

With the continuous innovations and development in communication technology and
intelligent transportation systems, a new generation of vehicular ad hoc networks (VANETs) …

Federated learning has become an emerging hot research field in industry because of its
ability to perform large-scale distributed learning while preserving data privacy. However …

Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …

Recent advances in deep learning (DL) have shown that data-driven insights can be used in
smart healthcare applications to improve the quality of life for patients. DL needs more data …

Clustered federated learning (CFL), as an important research branch of personalized
federated learning (FL), can better cope with the highly statistically heterogeneous federated …

Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It
enables distributed learning to train on cross-device data, achieving efficient performance …

Federated learning (FL) is a privacy-aware computing framework that enables multiple
clients to collaborate in solving machine learning problems. In real scenarios, non-IID data …

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 …