Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning
to address privacy concerns associated with centralized data storage and processing. VFL …

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 …

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

Vertical Federated Learning (VFL) enables multiple parties to collaboratively train a machine
learning model over vertically distributed datasets without data privacy leakage. However …

Vertical federated learning (VFL) system has recently become prominent as a concept to
process data distributed across many individual sources without the need to centralize it …

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 paradigm for predictive analytics,
empowering an organization (ie, task party) to enhance its predictive models through …

Vertical federated learning (VFL) enables multiple participants with different data features
and the same sample ID space to collaboratively train a model in a privacy-preserving way …

Vertical federated learning (VFL) has attracted greater and greater interest since it enables
multiple parties possessing non-overlapping features to strengthen their machine learning …

Autoencoders are popular neural networks that are able to compress high dimensional data
to extract relevant latent information. TabNet is a state-of-the-art neural network model …