Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …

In recent years, along with the blooming of Machine Learning (ML)-based applications and
services, ensuring data privacy and security have become a critical obligation. ML-based …

In response to growing concerns about user privacy, federated learning has emerged as a
promising tool to train statistical models over networks of devices while keeping data …

Federated learning (FL) has great potential for coalescing isolated data islands. It enables
privacy-preserving collaborative model training and addresses security and privacy …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

In federated learning (FL), data owners" share" their local data in a privacy preserving
manner in order to build a federated model, which in turn, can be used to generate revenues …

Federated Learning (FL) has emerged as a privacy-preserving distributed machine learning
paradigm. To motivate data owners to contribute towards FL, research on FL incentive …

This book provides a comprehensive and self-contained introduction to federated learning,
ranging from the basic knowledge and theories to various key applications. Privacy and …

Due to stricter data management regulations and large size of the training data, distributed
learning paradigm such as federated learning (FL) has gained attention recently. FL is …

Recent advances in federated learning (FL) have brought large-scale collaborative machine
learning opportunities for massively distributed clients with performance and data privacy …