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 …

This review highlights the efficacy of combining federated learning (FL) and transfer learning
(TL) for cancer detection via image analysis. By integrating these techniques, research has …

Vertical federated learning (VFL) provides a privacy-preserving method for machine
learning, enabling collaborative training across multiple institutions with vertically distributed …

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 …

In the healthcare field, cross-institutional collaboration can fasten medical research
progress. Vertical federated learning (VFL) addresses data heterogeneity across multiple …

Federated Learning (FL) is an evolving paradigm that enables multiple parties to
collaboratively train models without sharing raw data. Among its variants, Vertical Federated …

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

The need to safeguard data privacy and adhere to regulations such as GDPR creates data
silos and has prompted the emergence and widespread adoption of techniques for …

Federated learning, a pioneering paradigm, enables collaborative model training without
exposing users' data to central servers. Most existing federated learning systems necessitate …

Split Learning (SL) is widely regarded as a promising distributed machine learning
framework with superior privacy-preserving properties, lower communication and …