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 …

Various ensemble machine learning techniques have been widely studied and implemented
to construct the predictive models in different sciences, including bagging, boosting, and …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

Federated learning (FL) has garnered significant attention as a novel machine learning
technique that enables collaborative training among multiple parties without exposing raw …

The rapid spread of the Internet of Things (IoT), with billions of connected devices, has
generated huge amounts of data and asks for decentralized solutions for machine learning …

Simple Summary This study addresses the classification task in animal science, which helps
organize and analyze complex data, essential for making informed decisions. It introduces …

Imbalanced data classification is an important problem in the field of computer science.
Traditional classification algorithms often experience a decrease in accuracy when the data …

Federated learning (FL) is a decentralized machine learning approach whereby each device
is allowed to train local models, eliminating the requirement for centralized data collecting …

Federated learning is a framework in machine learning that allows for training a model while
maintaining data privacy. Moreover, it allows clients with their own data to collaborate in …

As a very popular framework, federated learning can help heterogeneous participants
cooperate training global models without the local data being exposed. It not only takes …