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 been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

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 …

Cross-platform recommendation aims to improve recommendation accuracy by gathering
heterogeneous features from different platforms. However, such cross-silo collaborations …

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

The traditional vertical federated learning schema suffers from two main issues: 1) restricted
applicable scope to overlapped samples and 2) high system challenge of real-time …

In recent years, federated learning has achieved remarkable results in the medical and
financial fields, but various attacks have always plagued federated learning. Data poisoning …

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 …