Cover Image

Backdoor Attack on Vertical Federated Graph Neural Network Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Jirui, Chen, Peng, Lu, Zhihui, Deng, Ruijun, Duan, Qiang, Zeng, Jianping
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

UIFV: Data Reconstruction Attack in Vertical Federated Learning
by: Yang, Jirui, et al.
Published: (2024)

InfoDecom: Decomposing Information for Defending Against Privacy Leakage in Split Inference
by: Deng, Ruijun, et al.
Published: (2025)

Heterogeneous Graph Backdoor Attack
by: Chen, Jiawei, et al.
Published: (2025)

Structure-Aware Distributed Backdoor Attacks in Federated Learning
by: Jian, Wang, et al.
Published: (2026)

Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning
by: Foroughi, Mohammad Hadi, et al.
Published: (2026)

Client-Side Patching against Backdoor Attacks in Federated Learning
by: Molina-Coronado, Borja
Published: (2024)

Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks
by: Alharbi, Ebtisaam, et al.
Published: (2025)

Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
by: Obioma, Chibueze Peace, et al.
Published: (2025)

A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks
by: Dai, Jiazhu, et al.
Published: (2025)

TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks
by: Hossain, Khondoker Murad, et al.
Published: (2024)

VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication
by: Yuan, Xun, et al.
Published: (2024)

AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and Structure Optimization
by: Chen, Yang, et al.
Published: (2024)

Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directions
by: Yang, Xiao, et al.
Published: (2024)

Unlearn to Relearn Backdoors: Deferred Backdoor Functionality Attacks on Deep Learning Models
by: Shin, Jeongjin, et al.
Published: (2024)

ReVeil: Unconstrained Concealed Backdoor Attack on Deep Neural Networks using Machine Unlearning
by: Alam, Manaar, et al.
Published: (2025)

Backdoor Graph Condensation
by: Wu, Jiahao, et al.
Published: (2024)

Unlearning Inversion Attacks for Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2025)

Prefix Probing: Lightweight Harmful Content Detection for Large Language Models
by: Yang, Jirui, et al.
Published: (2025)

BadImplant: Injection-based Multi-Targeted Graph Backdoor Attack
by: Khan, Md Nabi Newaz, et al.
Published: (2026)

ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks
by: Ren, Zhiyao, et al.
Published: (2025)

Invisible Backdoor Attack Through Singular Value Decomposition
by: Chen, Wenmin, et al.
Published: (2024)

Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning
by: Liu, Tao, et al.
Published: (2024)

Backdoor Vectors: a Task Arithmetic View on Backdoor Attacks and Defenses
by: Pawlak, Stanisław, et al.
Published: (2025)

SAB:A Stealing and Robust Backdoor Attack based on Steganographic Algorithm against Federated Learning
by: Xu, Weida, et al.
Published: (2024)

CUBA: Controlled Untargeted Backdoor Attack against Deep Neural Networks
by: Wu, Yinghao, et al.
Published: (2025)

UNIDOOR: A Universal Framework for Action-Level Backdoor Attacks in Deep Reinforcement Learning
by: Ma, Oubo, et al.
Published: (2025)

Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning
by: Lee, Seohyun, et al.
Published: (2025)

VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learning
by: Tan, Juntao, et al.
Published: (2025)

Revisiting Backdoor Attacks on Time Series Classification in the Frequency Domain
by: Huang, Yuanmin, et al.
Published: (2025)

Hijack Vertical Federated Learning Models As One Party
by: Qiu, Pengyu, et al.
Published: (2022)

Your Agent Can Defend Itself against Backdoor Attacks
by: Changjiang, Li, et al.
Published: (2025)

Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering
by: Lyu, Xingyu, et al.
Published: (2025)

Compromising Embodied Agents with Contextual Backdoor Attacks
by: Liu, Aishan, et al.
Published: (2024)

FedDefender: Backdoor Attack Defense in Federated Learning
by: Gill, Waris, et al.
Published: (2023)

Selection-Based Vulnerabilities: Clean-Label Backdoor Attacks in Active Learning
by: Zhi, Yuhan, et al.
Published: (2025)

Trusted Weights, Treacherous Optimizations? Optimization-Triggered Backdoor Attacks on LLMs
by: Wang, Yifei, et al.
Published: (2026)

Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
by: Abbahaddou, Yassine, et al.
Published: (2024)

LoBAM: LoRA-Based Backdoor Attack on Model Merging
by: Yin, Ming, et al.
Published: (2024)

Quantifying Privacy Leakage in Split Inference via Fisher-Approximated Shannon Information Analysis
by: Deng, Ruijun, et al.
Published: (2025)

BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting
by: Lin, Xiao, et al.
Published: (2024)