Federated learning is a distributed learning framework that takes full advantage of private
data samples kept on edge devices. In real-world federated learning systems, these data …

Federated Learning (FL) is a distributed approach for performing machine learning tasks. It
prevents data sharing by aggregating the models trained by distributed clients on the central …

Federated learning (FL) is an emerging distributed and privacy-preserving machine learning
framework. However, the performance of traditional FL methods is seriously impaired by the …

Federated Learning (FL) has emerged as a means of distributed learning using local data
stored at clients with a coordinating server. Recent studies showed that FL can suffer from …

Clustered Federated Learning (CFL) is a promising distributed learning framework that
addresses data heterogeneity issues across multiple clients by grouping clients and …

Federated learning (FL) is a distributed machine learning framework that allows resource-
constrained clients to train a global model jointly without compromising data privacy …

Federated learning (FL) is an emerging distributed machine learning paradigm enabling
collaborative model training on decentralized devices without exposing their local data. A …

Federated learning (FL) is an emerging decentralized machine learning framework enabling
private global model training by collaboratively leveraging local client data without …

Federated Learning (FL) is a type of distributed machine learning, which avoids sharing
privacy and sensitive data with a central server. Despite the advances in FL, current …

Federated Learning (FL) has achieved significant achievements recently, enabling
collaborative model training on distributed data over edge devices. Iterative gradient or …