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

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

Large Language Models (LLMs), such as ChatGPT, LLaMA, GLM, and PaLM, have
exhibited remarkable performances across various tasks in recent years. However, LLMs …

Abstract 5 G is the fifth generation of cellular networks. It enables billions of connected
devices to gather and share information in real time; a key facilitator in Industrial Internet of …

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-
world tasks on graph data, consisting of node features and the adjacent information between …

Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly
benefited human society. Among various AI technologies, Federated Learning (FL) stands …

Abstract Graph Neural Networks (GNNs) have achieved remarkable performance by taking
advantage of graph data. The success of GNN models always depends on rich features and …

Deep learning (DL) has shown outstanding performances in many fields, including remote
sensing (RS). DL is turning into an essential tool for the RS research community. Recently …

Vertical federated learning (VFL) is a promising category of federated learning for the
scenario where data is vertically partitioned and distributed among parties. VFL enriches the …