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) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

This paper proposes a privacy-preserving data fusion method for traffic state estimation
(TSE). Unlike existing works that assume all data sources to be accessible by a single …

Federated learning (FL) has emerged as a promising distributed learning paradigm with an
added advantage of data privacy. With the growing interest in collaboration among data …

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm
where different parties collaboratively learn models using partitioned features of shared …

Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning
to address privacy concerns associated with centralized data storage and processing. VFL …

The need to safeguard data privacy and adhere to regulations such as GDPR creates data
silos and has prompted the emergence and widespread adoption of techniques for …

Vertical Federated Learning (VFL) has emerged as a crucial privacy-preserving learning
paradigm that involves training models using distributed features from shared samples …

Most work in privacy-preserving federated learning (FL) has been focusing on horizontally
partitioned datasets where clients share the same sets of features and can train complete …