Federated Learning (FL) is a method of training machine learning models on private data
distributed over a large number of possibly heterogeneous clients such as mobile phones …

Federated learning enables resource-constrained edge compute devices, such as mobile
phones and IoT devices, to learn a shared model for prediction, while keeping the training …

Federated Learning allows training machine learning models by using the computation and
private data resources of a large number of distributed clients such as smartphones and IoT …

Federated learning (FL) is increasingly becoming the norm for training models over
distributed and private datasets. Major service providers rely on FL to improve services such …

Federated learning (FL) enables collaboratively training deep learning models on
decentralized data. However, there are three types of heterogeneities in FL setting bringing …

Abstract The Federated Learning (FL) paradigm is known to face challenges under
heterogeneous client data. Local training on non-iid distributed data results in deflected …

Federated learning (FL) is an emerging paradigm to train a global deep neural network
(DNN) model by collaborative clients that store their private data locally through the …

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm with
enhanced privacy, aiming to achieve a" good" ML model for as many as participants while …

Federated learning is a popular distributed machine learning paradigm with enhanced
privacy. Its primary goal is learning a global model that offers good performance for the …

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 …