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 is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …

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 …

Federated learning (FL) has gained increasing attention due to its ability to collaboratively
train while protecting client data privacy. However, vanilla FL cannot adapt to client …

Federated learning (FL) enables participating parties to collaboratively build a global model
with boosted utility without disclosing private data information. Appropriate protection …

In a vertical federated learning (VFL) scenario where features and models are split into
different parties, it has been shown that sample-level gradient information can be exploited …

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 …

Training deep neural networks often forces users to work in a distributed or outsourced
setting, accompanied with privacy concerns. Split learning aims to address this concern by …

Federated learning has received a great deal of research attention recently, with privacy
protection becoming a key factor in the development of artificial intelligence. Federated …