Vertical federated learning (VFL) is a privacy-preserving distribution learning paradigm that
enables participants, owning different features of the same sample space to train a machine …

Federated Graph Neural Network (FedGNN) is a privacy-preserving machine learning
technology that combines federated learning (FL) and graph neural networks (GNNs). It …

Vertical Federated Learning (VFL) facilitates collaboration on model training among multiple
parties, each owning partitioned features of the distributed dataset. Although backdoor …

Abstract Vertical Federated Learning (VFL) focuses on handling vertically partitioned data
over FL participants. Recent studies have discovered a significant vulnerability in VFL to …

Vertical federated learning (VFL) is a cloud-edge collaboration paradigm that enables edge
nodes, comprising resource-constrained Internet of Things (IoT) devices, to cooperatively …

A novel asynchronous federated architecture is proposed in this work for data prediction,
aggregation, and inference with a use case for fast short-term instability mitigation in …

Vertical federated learning (VFL), where each participating client holds a subset of data
features, has found numerous applications in finance, healthcare, and IoT systems …

Abstract Graph Neural Network based Vertical Federated Learning (GVFL) facilitates data
collaboration while preserving data privacy by learning GNN-based node representations …

Collaborative inference combines diverse features contributed by various agents to improve
prediction accuracy. However, it is vulnerable to adversarial attacks, where attackers …