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 …

Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …

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 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 one of the leading paradigms of modern times with higher privacy
guarantees than any other digital solution. Since its inception in 2016, FL has been …

Vertical Federated Learning (VFL) is a trending collaborative machine learning model
training solution. Existing industrial frameworks employ secure multi-party computation …

Federated learning (FL) is the most popular distributed machine learning technique. FL
allows machine-learning models to be trained without acquiring raw data to a single point for …

Federated Learning (FL) represents the de facto approach for distributed training of machine
learning models. Nevertheless, researchers have identified several security and privacy FL …

Vertical federated learning (VFL) enables multiple parties with disjoint features of a common
user set to train a machine learning model without sharing their private data. Tree-based …

Vertical federated learning is a trending solution for multi-party collaboration in training
machine learning models. Industrial frameworks adopt secure multi-party computation …