The data generated and stored in mobile devices owned by individuals as well as in various
organizations contains a large amount of valuable and important information that can be …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

Due to privacy protection, the conventional machine learning approaches, which upload all
data to a central location, has become less feasible. Federated learning, a privacy …

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 …

Federated Learning (FL) is a privacy preserving machine learning scheme, where training
happens with data federated across devices and not leaving them to sustain user privacy …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Federated Learning is a promising technique for preserving data privacy that enables
communication between distributed nodes without the need for a central server. Previously …

Federated learning enables machine learning models to learn from private decentralized
data without compromising privacy. The standard formulation of federated learning produces …

With the growing concern on data privacy and security, it is undesirable to collect data from
all users to perform machine learning tasks. Federated learning, a decentralized learning …

Federated learning has received attention for its efficiency and privacy benefits, in settings
where data is distributed among devices. Although federated learning shows significant …