Federated learning (FL) involves training a model over massive distributed devices, while
keeping the training data localized. This form of collaborative learning exposes new …

Federated learning (FL) involves training a model over massive distributed devices, while
keeping the training data localized and private. This form of collaborative learning exposes …

As a promising approach to deal with distributed data, Federated Learning (FL) achieves
major advancements in recent years. FL enables collaborative model training by exploiting …

Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …

Federated learning (FL) has attracted increasing attention as a promising approach to
driving a vast number of end devices with artificial intelligence. However, it is very …

Federated learning (FL) has been a popular method to achieve distributed machine learning
among numerous devices without sharing their data to a cloud server. FL aims to learn a …

When the available hardware cannot meet the memory and compute requirements to
efficiently train high performing machine learning models, a compromise in either the …

The federated learning (FL) framework trains a machine learning model using decentralized
data stored at edge client devices by periodically aggregating locally trained models …

Federated learning (FL) has been widely recognized as a promising approach by enabling
individual end-devices to cooperatively train a global model without exposing their own …

Federated learning (FL) enables edge-devices to collaboratively learn a model without
disclosing their private data to a central aggregating server. Most existing FL algorithms …