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 …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

Evaluation is a systematic approach to assessing how well a system achieves its intended
purpose. Federated learning (FL) is a novel paradigm for privacy-preserving machine …

Simple Summary This study addresses the classification task in animal science, which helps
organize and analyze complex data, essential for making informed decisions. It introduces …

In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm
where different parties collaboratively learn models using partitioned features of shared …

Generative Adversarial Networks (GANs) have achieved state-of-the-art results in tabular
data synthesis, under the presumption of direct accessible training data. Vertical Federated …

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 …

Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that
allows participants with different features of the same group of users to accomplish …

Vertical federated learning (VFL) has recently emerged as an appealing distributed
paradigm empowering multi-party collaboration for training high-quality models over …