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 …

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 …

Due to the rising concerns on privacy protection, how to build machine learning (ML) models
over different data sources with security guarantees is gaining more popularity. Vertical …

Federated learning allows multiple parties to build machine learning models collaboratively
without exposing data. In particular, vertical federated learning (VFL) enables participating …

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

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 …

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 …