FedGMark: Certifiably Robust Watermarking for Federated Graph Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Yang, Yuxin, Li, Qiang, Hong, Yuan, Wang, Binghui |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses
by: Yang, Yuxin, et al.
Published: (2024)
by: Yang, Yuxin, et al.
Published: (2024)
Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learning
by: Nie, Chenfei, et al.
Published: (2024)
by: Nie, Chenfei, et al.
Published: (2024)
A Learning-Based Attack Framework to Break SOTA Poisoning Defenses in Federated Learning
by: Yang, Yuxin, et al.
Published: (2024)
by: Yang, Yuxin, et al.
Published: (2024)
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective
by: Wang, Zifan, et al.
Published: (2024)
by: Wang, Zifan, et al.
Published: (2024)
Towards Strong Certified Defense with Universal Asymmetric Randomization
by: Hong, Hanbin, et al.
Published: (2025)
by: Hong, Hanbin, et al.
Published: (2025)
Learning Robust and Privacy-Preserving Representations via Information Theory
by: Zhang, Binghui, et al.
Published: (2024)
by: Zhang, Binghui, et al.
Published: (2024)
A Certified Robust Watermark For Large Language Models
by: Feng, Xianheng, et al.
Published: (2024)
by: Feng, Xianheng, et al.
Published: (2024)
Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable Confidence
by: Hong, Hanbin, et al.
Published: (2023)
by: Hong, Hanbin, et al.
Published: (2023)
Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks
by: Zhang, Xinyu, et al.
Published: (2023)
by: Zhang, Xinyu, et al.
Published: (2023)
Watermarking Graph Neural Networks via Explanations for Ownership Protection
by: Downer, Jane, et al.
Published: (2025)
by: Downer, Jane, et al.
Published: (2025)
Watermark Robustness and Radioactivity May Be at Odds in Federated Learning
by: Huang, Leixu, et al.
Published: (2025)
by: Huang, Leixu, et al.
Published: (2025)
Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks
by: Li, Jiate, et al.
Published: (2025)
by: Li, Jiate, et al.
Published: (2025)
AGNNCert: Defending Graph Neural Networks against Arbitrary Perturbations with Deterministic Certification
by: Li, Jiate, et al.
Published: (2025)
by: Li, Jiate, et al.
Published: (2025)
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks
by: Noorbakhsh, Sayedeh Leila, et al.
Published: (2024)
by: Noorbakhsh, Sayedeh Leila, et al.
Published: (2024)
Backdoor Attacks on Discrete Graph Diffusion Models
by: Wang, Jiawen, et al.
Published: (2025)
by: Wang, Jiawen, et al.
Published: (2025)
FedRE: Robust and Effective Federated Learning with Privacy Preference
by: Xiao, Tianzhe, et al.
Published: (2025)
by: Xiao, Tianzhe, et al.
Published: (2025)
DRGW: Learning Disentangled Representations for Robust Graph Watermarking
by: Li, Jiasen, et al.
Published: (2026)
by: Li, Jiasen, et al.
Published: (2026)
FedGIG: Graph Inversion from Gradient in Federated Learning
by: Xiao, Tianzhe, et al.
Published: (2024)
by: Xiao, Tianzhe, et al.
Published: (2024)
Practical, Generalizable and Robust Backdoor Attacks on Text-to-Image Diffusion Models
by: Dai, Haoran, et al.
Published: (2025)
by: Dai, Haoran, et al.
Published: (2025)
Certifiably Robust Image Watermark
by: Jiang, Zhengyuan, et al.
Published: (2024)
by: Jiang, Zhengyuan, et al.
Published: (2024)
FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
by: Gu, Hanlin, et al.
Published: (2024)
by: Gu, Hanlin, et al.
Published: (2024)
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence
by: Feng, Shuya, et al.
Published: (2024)
by: Feng, Shuya, et al.
Published: (2024)
On Google's SynthID-Text LLM Watermarking System: Theoretical Analysis and Empirical Validation
by: Omidi, Romina, et al.
Published: (2026)
by: Omidi, Romina, et al.
Published: (2026)
Robust Client-Server Watermarking for Split Federated Learning
by: Tang, Jiaxiong, et al.
Published: (2025)
by: Tang, Jiaxiong, et al.
Published: (2025)
A Reinforcement Learning Framework for Robust and Secure LLM Watermarking
by: An, Li, et al.
Published: (2025)
by: An, Li, et al.
Published: (2025)
Collective Certified Robustness against Graph Injection Attacks
by: Lai, Yuni, et al.
Published: (2024)
by: Lai, Yuni, et al.
Published: (2024)
FedFDP: Fairness-Aware Federated Learning with Differential Privacy
by: Ling, Xinpeng, et al.
Published: (2024)
by: Ling, Xinpeng, et al.
Published: (2024)
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
by: Xie, Chulin, et al.
Published: (2022)
by: Xie, Chulin, et al.
Published: (2022)
RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection
by: Xu, Yang, et al.
Published: (2024)
by: Xu, Yang, et al.
Published: (2024)
FedSGT: Exact Federated Unlearning via Sequential Group-based Training
by: Zhang, Bokang, et al.
Published: (2025)
by: Zhang, Bokang, et al.
Published: (2025)
Practicable Black-box Evasion Attacks on Link Prediction in Dynamic Graphs -- A Graph Sequential Embedding Method
by: Li, Jiate, et al.
Published: (2024)
by: Li, Jiate, et al.
Published: (2024)
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
by: Li, Youpeng, et al.
Published: (2024)
by: Li, Youpeng, et al.
Published: (2024)
FedCC: Robust Federated Learning against Model Poisoning Attacks
by: Jeong, Hyejun, et al.
Published: (2022)
by: Jeong, Hyejun, et al.
Published: (2022)
Harmless Backdoor-based Client-side Watermarking in Federated Learning
by: Luo, Kaijing, et al.
Published: (2024)
by: Luo, Kaijing, et al.
Published: (2024)
FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks
by: Chen, Jian, et al.
Published: (2025)
by: Chen, Jian, et al.
Published: (2025)
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
by: Tan, Zihan, et al.
Published: (2024)
by: Tan, Zihan, et al.
Published: (2024)
"Training robust watermarking model may hurt authentication!'' Exploring and Mitigating the Identity Leakage in Robust Watermarking
by: Zhang, Xinyu, et al.
Published: (2026)
by: Zhang, Xinyu, et al.
Published: (2026)
SiGRRW: A Single-Watermark Robust Reversible Watermarking Framework with Guiding Strategy
by: Xu, Zikai, et al.
Published: (2026)
by: Xu, Zikai, et al.
Published: (2026)
Robustness of Watermarking on Text-to-Image Diffusion Models
by: Wu, Xiaodong, et al.
Published: (2024)
by: Wu, Xiaodong, et al.
Published: (2024)
CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization
by: Wang, Derui, et al.
Published: (2025)
by: Wang, Derui, et al.
Published: (2025)
Similar Items
-
Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses
by: Yang, Yuxin, et al.
Published: (2024) -
Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learning
by: Nie, Chenfei, et al.
Published: (2024) -
A Learning-Based Attack Framework to Break SOTA Poisoning Defenses in Federated Learning
by: Yang, Yuxin, et al.
Published: (2024) -
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective
by: Wang, Zifan, et al.
Published: (2024) -
Towards Strong Certified Defense with Universal Asymmetric Randomization
by: Hong, Hanbin, et al.
Published: (2025)