Federated learning (FL) supports training models on geographically distributed devices.
However, traditional FL systems adopt a centralized synchronous strategy, putting high …

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) enables multiple devices to collaboratively train a shared model
while preserving data privacy. However, despite its emerging applications in many areas …

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) provides a distributed learning framework for multiple participants to
collaborate learning without sharing raw data. In many practical FL scenarios, participants …

Federated Learning (FL) is a novel distributed machine learning which allows thousands of
edge devices to train model locally without uploading data concentrically to the server. But …

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

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) over mobile devices is a promising distributed learning paradigm for
various mobile applications. However, practical deployment of FL over mobile devices is …

Federated Learning (FL) is a newly emerged decentralized machine learning (ML)
framework that combines on-device local training with server-based model synchronization …