Federated learning (FL) is a decentralized and privacy-preserving machine learning
technique in which a group of clients collaborate with a server to learn a global model …

Classical federated learning (FL) enables training machine learning models without sharing
data for privacy preservation, but heterogeneous data characteristic degrades the …

Federated learning (FL) enables distributed optimization of machine learning models while
protecting privacy by independently training local models on each client and then …

Federated Learning (FL) is a promising framework for performing privacy-preserving,
distributed learning with a set of clients. However, the data distribution among clients often …

Federated Averaging (FedAvg) remains the most popular algorithm for Federated Learning
(FL) optimization due to its simple implementation, stateless nature, and privacy guarantees …

Federated learning is an emerging learning paradigm where multiple clients collaboratively
train a machine learning model in a privacy-preserving manner. Personalized federated …

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 (FL) is an emerging machine learning paradigm in which a central
server coordinates multiple participants (aka FL clients) to train a model collaboratively on …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

Newton-type methods are popular in federated learning due to their fast convergence. Still,
they suffer from two main issues, namely: low communication efficiency and low privacy due …