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 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 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 and privacy-preserving machine learning
framework. However, the performance of traditional FL methods is seriously impaired by the …

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 emerged as a new paradigm for training machine learning
models distributively without sacrificing data security and privacy. Learning models on edge …

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

Federated learning is highly valued due to its high-performance computing in distributed
environments while safeguarding data privacy. To address resource heterogeneity …

Federated Learning (FL) is an emerging research field that yields a global trained model
from different local clients without violating data privacy. Existing FL techniques often ignore …

This paper proposes a client selection (CS) method to tackle the communication bottleneck
of federated learning (FL) while concurrently coping with FL's data heterogeneity issue …