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 a distributed learning paradigm that can coordinate
heterogeneous edge devices to perform model training without sharing private data. While …

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

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) achieves great popularity in broad areas as a powerful interface to
offer intelligent services to customers while maintaining data privacy. Nevertheless, FL faces …

Federated learning (FL) is a novel machine learning that performs distributed training locally
on devices and aggregating the local models into a global one. The limited network …

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

In Federated Learning (FL) client devices connected over the internet collaboratively train a
machine learning model without sharing their private data with a central server or with other …