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 …

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

Federated learning (FL) has been proposed to allow collaborative training of machine
learning (ML) models among multiple parties to keep their data private and only model …

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 …

Federated learning (FL) has recently emerged as a striking framework for allowing machine
and deep learning models with thousands of participants to have distributed training to …

联邦学习是顺应大数据时代和人工智能技术发展而兴起的一种协调多个参与方共同训练模型的
机制. 它允许各个参与方将数据保留在本地, 在打破数据孤岛的同时保证参与方对数据的控制权 …

Federated Learning (FL) has emerged as a promising distributed learning paradigm with an
added advantage of data privacy. With the growing interest in having collaboration among …

Vertical federated learning (VFL) is a promising category of federated learning for the
scenario where data is vertically partitioned and distributed among parties. VFL enriches the …

In a vertical federated learning (VFL) system consisting of a central server and many
distributed clients, the training data are vertically partitioned such that different features are …