Federated learning (FL) refers to a system in which a central aggregator coordinates the
efforts of several clients to solve the issues of machine learning. This setting allows the …

The increasing volume of personal and sensitive data being harvested by data controllers
makes it increasingly necessary to use the cloud not just to store the data, but also to …

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 …

Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …

The privacy-preserving federated learning for vertically partitioned (VP) data has shown
promising results as the solution of the emerging multiparty joint modeling application, in …

Abstract Internet of Things (IoT) is going to create a world where physical objects are
seamlessly integrated into information networks in order to provide advanced and intelligent …

Secure multi-party computation (MPC) allows a group of mutually distrustful parties to
compute a joint function on their inputs without revealing any information beyond the result …

We introduce PyVertical, a framework supporting vertical federated learning using split
neural networks. The proposed framework allows a data scientist to train neural networks on …

Personal data are often collected and processed in a decentralized fashion, within different
contexts. For instance, with the emergence of distributed applications, several providers are …

In recent years, privacy-preserving data mining has been studied extensively, because of
the wide proliferation of sensitive information on the internet. A number of algorithmic …