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

Conversion rate (CVR) estimation aims to predict the probability of conversion event after a
user has clicked an ad. Typically, online publisher has user browsing interests and click …

Vertical federated learning (VFL), a variant of Federated Learning (FL), has recently drawn
increasing attention as the VFL matches the enterprises' demands of leveraging more …

Federated learning (FL) enables collaborative learning across multiple participants without
exposing sensitive personal data. However, the distributed nature of FL and unvetted …

As an emerging privacy-preserving approach to leveraging cross-platform user interactions,
vertical federated learning (VFL) has been increasingly applied in recommender systems …

Recently, vertical federated learning (VFL) has gained more attention due to its ability to
combine various valuable features for better model performance without violating privacy …

In the realm of industrial edge computing, a novel server architecture known as SoC-Cluster,
characterized by its aggregation of numerous mobile systems-on-chips (SoCs), has …

SoC-Cluster, a novel server architecture composed of massive mobile system-on-chips
(SoCs), is gaining popularity in industrial edge computing due to its energy efficiency and …

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 …