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 …

Vertical Federated Learning (VFL) is widely utilized in real-world applications to enable
collaborative learning while protecting data privacy and safety. However, previous works …

Federated learning is an emerging paradigm that enables multiple organizations to jointly
train a model without revealing their private data. As an important variant, vertical federated …

Federated learning (FL) enables distributed participants to collaboratively learn a global
model without revealing their private data to each other. Recently, vertical FL, where the …

Federated learning (FL) has emerged as a practical solution to tackle data silo issues
without compromising user privacy. One of its variants, vertical federated learning (VFL), has …

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 …

Vertical Federated Learning (VFL) is a category of Federated Learning in which models are
trained collaboratively among parties with vertically partitioned data. Typically, in a VFL …

Vertical federated learning (vFL) has gained much attention and been deployed to solve
machine learning problems with data privacy concerns in recent years. However, some …

Vertical Federated Learning (VFL) is a federated learning paradigm where multiple
participants, who share the same set of samples but hold different features, jointly train …

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data
collaboration without revealing their private data to each other. Recently, vertical FL, where …