With the widespread use of Internet of things (IoT), mobile phones, connected devices and
artificial intelligence (AI), recommender systems (RSs) have become a booming technology …

To protect user privacy and meet law regulations, federated (machine) learning is obtaining
vast interests in recent years. The key principle of federated learning is training a machine …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

With the popularity of Internet of Things (IoT), Point‐of‐Interest (POI) recommendation has
become an important application for location‐based services (LBS). Meanwhile, there is an …

Recommender systems are collecting and analyzing user data to provide better user
experience. However, several privacy concerns have been raised when a recommender …

In recent years, recommender systems play a pivotal role in helping users identify the most
suitable items that satisfy personal preferences. As user-item interactions can be naturally …

In this work, we present a federated version of the state-of-the-art Neural Collaborative
Filtering (NCF) approach for item recommendations. The system, named FedNCF, enables …

In recent years, recommender systems are crucially important for the delivery of
personalized services that satisfy users' preferences. With personalized recommendation …

Federated analytics (FA) is a privacy-preserving framework for computing data analytics
over multiple remote parties (eg, mobile devices) or silo-ed institutional entities (eg …

Federated learning (FL) has received a great deal of research attention in the context of
privacy protection restrictions. By jointly training deep learning models, a variety of training …