As societal concerns on data privacy recently increase, we have witnessed data silos among
multiple parties in various applications. Federated learning emerges as a new learning …

Federated learning (FL) is a privacy-preserving distributed learning approach that allows
multiple parties to jointly build machine learning models without disclosing sensitive data …

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 is a collaborative machine learning framework to train deep
leaning models on vertically partitioned data with privacy-preservation. It attracts much …

In recent years, federated learning has attracted more and more attention as it could
collaboratively train a global model without gathering the users' raw data. It has brought …

Federated learning enables multiple parties to collaboratively learn a model without
exchanging their data. While most existing federated learning algorithms need many rounds …

Due to the rising concerns on privacy protection, how to build machine learning (ML) models
over different data sources with security guarantees is gaining more popularity. Vertical …

Federated learning allows multiple parties to build machine learning models collaboratively
without exposing data. In particular, vertical federated learning (VFL) enables participating …

Vertical Federated Learning (vFL) allows multiple parties that own different attributes (eg
features and labels) of the same data entity (eg a person) to jointly train a model. To prepare …

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