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 …

There has been tremendous growth in the field of AI and machine learning. The
developments across these fields have resulted in a considerable increase in other FinTech …

With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

The growing application of machine learning (ML) techniques in healthcare has led to
increased interest in federated learning (FL), which enables the secure and private training …

Vertical federated learning (VFL) is a promising category of federated learning for the
scenario where data is vertically partitioned and distributed among parties. VFL enriches the …

The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …

The emergence of edge computing guarantees the combination of the Internet of Things
(IoT) and artificial intelligence (AI). The vertical federated learning (VFL) framework, usually …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …

Federated learning (FL) has emerged as a practical solution to tackle data silo issues
without compromising user privacy. One of its variants, vertical federated learning (VFL), has …

Federated Learning (FL) is a decentralized learning mechanism that has attracted
increasing attention due to its achievements in computational efficiency and privacy …