Federated Machine Learning (FL) has received considerable attention in recent years. FL
benchmarks are predominantly explored in either simulated systems or data center …

Cross-device federated learning (FL) involves FLClients sharing their model updates to a
global server for aggregation, which may result in a single point of failure as it becomes …

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) enables edge nodes to collaboratively contribute to constructing a
global model without sharing their data. This is accomplished by devices computing local …

Federated learning (FL) enabled creating models that are competitive to centralized
machine learning models, without compromising user privacy. Participating FL clients train …

Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes, and end
devices, has shown great potential in bringing data processing closer to the data sources …

Federated Learning (FL) is a distributed learning paradigm that can coordinate
heterogeneous edge devices to perform model training without sharing private raw data …

Federated Learning has become the de facto paradigm for training AI models under a
distributed modality where the computational effort is spread across several clients without …

Most Federated Learning (FL) algorithms proposed to date obtain the global model by
aggregating multiple local models that typically share the same architecture, thus …

Federated learning (FL) has emerged in edge computing to address limited bandwidth and
privacy concerns of traditional cloud-based centralized training. However, the existing FL …