Vertical federated learning is designed to protect user privacy by building local models over
disparate datasets and transferring intermediate parameters without directly revealing the …

Vertical federated learning (VFL), a variant of federated learning, has recently attracted
increasing attention. An active party having the true labels jointly trains a model with other …

Abstract Vertical Federated Learning (VFL) is a collaborative machine learning paradigm
that enables multiple participants to jointly train a model on their private data without sharing …

Federated Learning (FL) is designed to protect the data privacy of each client during the
training process by transmitting only models instead of the original data. However, the …

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …

As a new machine learning (ML) paradigm, federated learning (FL) empowers different
participants to jointly train a more effective model than traditional ML. Unlike horizontal FL …

Research on federated learning has continued to develop over the past few years. Many
federated learning algorithms and frameworks have been developed to ensure model …

Vertical federated learning (VFL) is an increasingly popular, yet understudied, collaborative
learning technique. In VFL, features and labels are distributed among different participants …

As societal concerns on data privacy recently increase, we have witnessed data silos among
multiple parties in various applications. Federated learning emerges as a new learning …

Federated learning (FL) has been used as a promising approach to breaking the dilemma
between the data privacy and the learning from large collections of distributed data. Without …