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) and split learning (SL) are two emerging collaborative learning
methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT) …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

The fast growth of pre-trained models (PTMs) has brought natural language processing to a
new era, which has become a dominant technique for various natural language processing …

We propose LESS-VFL, a communication-efficient feature selection method for distributed
systems with vertically partitioned data. We consider a system of a server and several parties …

AI progress is creating a growing range of risks and opportunities, but it is often unclear how
they should be navigated. In many cases, the barriers and uncertainties faced are at least …

In a vertical federated learning (VFL) system consisting of a central server and many
distributed clients, the training data are vertically partitioned such that different features are …

We describe a threat model under which a split network-based federated learning system is
susceptible to a model inversion attack by a malicious computational server. We …

Split learning is a distributed training framework that allows multiple parties to jointly train a
machine learning model over vertically partitioned data (partitioned by attributes). The idea …

This letter studies a vertical federated edge learning (FEEL) system for collaborative
objects/human motion recognition by exploiting the distributed integrated sensing and …