With the ever-evolving concerns on privacy protection, vertical federated learning (FL),
where participants own non-overlapping features for the same set of instances, is becoming …

With the advent of smart devices, smartphones, and smart everything, the Internet of Things
(IoT) has emerged with an incredible impact on the industries and human life. The IoT …

Federated Learning (FL) is an emerging technique for collaboratively training machine
learning models on distributed data under privacy constraints. However, recent studies have …

Federated learning (FL) ensures multi-party can train a model together while avoiding
privacy leakage. Our vertical federated learning (VFL) task tackles the following scenarios: i) …

Federated learning is an improved distributed machine learning approach for privacy
preservation. All clients collaboratively train the model using on-device data, and the …

Vertical federated learning (VFL), a variant of Federated Learning (FL), has recently drawn
increasing attention as the VFL matches the enterprises' demands of leveraging more …

Federated learning (FL) aims to address the challenges of data silos and privacy protection
in artificial intelligence. Vertically federated learning (VFL) with independent feature spaces …

Federated learning is a new type of distributed learning framework that allows multiple
participants to share training results without revealing their data privacy. As data privacy …

Federated learning (FL) has recently attracted much attention due to its superior
performance in privacy protection when processing data from different terminals. However …

With the popularity of IoT (Internet of Things) applications, edge computing has received lots
of attention. To meet data privacy protection requirements of edge nodes and cope with their …