The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …

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 (FL) is an emerging distributed machine learning paradigm that enables
collaborative training of machine learning models over decentralized devices without …

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

Federated Learning (FL) is a distributed approach where numerous devices train a shared
global model for Machine Learning (ML) tasks. At every training round, the client devices …

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

Federated learning (FL) enables distributed mobile devices to collaboratively learn a shared
model without exposing their raw data. However, heterogeneous devices usually have …

Federated learning (FL) is a decentralized machine learning architecture, which leverages a
large number of remote devices to learn a joint model with distributed training data …

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 …