Vertical federated learning (VFL) is a privacy preserving collaborative machine learning
technique designed for distributed learning scenarios in which data from different parties …

Existing work on privacy-preserving machine learning with Secure Multiparty Computation
(MPC) is almost exclusively focused on model training and on inference with trained models …

Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once
collected and processed through Machine Learning algorithms, enable AI-based services at …

In many real-world unsupervised learning applications, given data with balanced
distribution, that is, there are an approximately equal number of instances in each class, we …

For optimal results, retrieving a relevant feature from a microarray dataset has become a hot
topic for researchers involved in the study of feature selection (FS) techniques. The aim of …

Biological data, including gene expression data, are generally high-dimensional and require
efficient, generalizable, and scalable machine-learning methods to discover complex …

The demand for the privacy-preserving survival analysis of medical data integrated from
multiple institutions or countries has been increased. However, sharing the original medical …

Recently, data collaboration (DC) analysis has been developed for privacy-preserving
integrated analysis across multiple institutions. DC analysis centralizes individually …

Multi-source data fusion, in which multiple data sources are jointly analyzed to obtain
improved information, has attracted considerable research attention. Data confidentiality and …

Imbalanced classification is a challenging problem in machine learning and data mining.
Oversampling methods, such as the Synthetic Minority Oversampling Technique (SMOTE) …