The rapid advances in the Internet of Things (IoT) have promoted a revolution in
communication technology and offered various customer services. Artificial intelligence (AI) …

Federated learning (FL) offers distributed machine learning on edge devices. However, the
FL model raises privacy concerns. Various techniques, such as homomorphic encryption …

Vertical federated learning (VFL) is an increasingly popular, yet understudied, collaborative
learning technique. In VFL, features and labels are distributed among different participants …

Cross-silo privacy-preserving machine learning (PPML) adopt; Partial Homomorphic
Encryption (PHE) for secure data combination and high-quality model training across …

Vertical federated learning (VFL) enables multiple parties with disjoint features of a common
user set to train a machine learning model without sharing their private data. Tree-based …

In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …

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) is a federated learning paradigm where multiple
participants, who share the same set of samples but hold different features, jointly train …

Federated learning presents a compelling approach to training artificial intelligence systems
in decentralized settings, prioritizing data safety over traditional centralized training …

Vertical Federated Learning (VFL) is a crucial paradigm for training machine learning
models on feature-partitioned, distributed data. However, due to privacy restrictions, few …