Zobrazeno 1 - 10
of 556
pro vyhledávání: '"Wang Binghui"'
Federated graph learning (FedGL) is an emerging learning paradigm to collaboratively train graph data from various clients. However, during the development and deployment of FedGL models, they are susceptible to illegal copying and model theft. Backd
Externí odkaz:
http://arxiv.org/abs/2410.17533
Numerous explanation methods have been recently developed to interpret the decisions made by deep neural network (DNN) models. For image classifiers, these methods typically provide an attribution score to each pixel in the image to quantify its cont
Externí odkaz:
http://arxiv.org/abs/2409.16429
Federated learning (FL) is an emerging collaborative learning paradigm that aims to protect data privacy. Unfortunately, recent works show FL algorithms are vulnerable to the serious data reconstruction attacks. However, existing works lack a theoret
Externí odkaz:
http://arxiv.org/abs/2408.12119
Federated learning (FL) is an emerging distributed learning paradigm without sharing participating clients' private data. However, existing works show that FL is vulnerable to both Byzantine (security) attacks and data reconstruction (privacy) attack
Externí odkaz:
http://arxiv.org/abs/2407.19703
Federated Learning (FL) is a novel client-server distributed learning framework that can protect data privacy. However, recent works show that FL is vulnerable to poisoning attacks. Many defenses with robust aggregators (AGRs) are proposed to mitigat
Externí odkaz:
http://arxiv.org/abs/2407.15267
Autor:
Feng, Shuya, Mohammady, Meisam, Hong, Hanbin, Yan, Shenao, Kundu, Ashish, Wang, Binghui, Hong, Yuan
Differentially private federated learning (DP-FL) is a promising technique for collaborative model training while ensuring provable privacy for clients. However, optimizing the tradeoff between privacy and accuracy remains a critical challenge. To ou
Externí odkaz:
http://arxiv.org/abs/2407.14710
Autor:
Behnam, Arman, Wang, Binghui
Graph neural network (GNN) explainers identify the important subgraph that ensures the prediction for a given graph. Until now, almost all GNN explainers are based on association, which is prone to spurious correlations. We propose {\name}, a GNN cau
Externí odkaz:
http://arxiv.org/abs/2407.09378
Federated graph learning (FedGL) is an emerging federated learning (FL) framework that extends FL to learn graph data from diverse sources. FL for non-graph data has shown to be vulnerable to backdoor attacks, which inject a shared backdoor trigger i
Externí odkaz:
http://arxiv.org/abs/2407.08935
Explainable Graph Neural Network (GNN) has emerged recently to foster the trust of using GNNs. Existing GNN explainers are developed from various perspectives to enhance the explanation performance. We take the first step to study GNN explainers unde
Externí odkaz:
http://arxiv.org/abs/2406.03193
Graph Neural Networks (GNNs) have gained popularity in numerous domains, yet they are vulnerable to backdoor attacks that can compromise their performance and ethical application. The detection of these attacks is crucial for maintaining the reliabil
Externí odkaz:
http://arxiv.org/abs/2403.18136