PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Gao, Jiashi, Wang, Ziwei, Zhao, Xiangyu, Shi, Xinming, Yao, Xin, Wei, Xuetao |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect
por: Gao, Jiashi, et al.
Publicado: (2023)
por: Gao, Jiashi, et al.
Publicado: (2023)
Stealthy Poisoning Attacks Bypass Defenses in Regression Settings
por: Carnerero-Cano, Javier, et al.
Publicado: (2026)
por: Carnerero-Cano, Javier, et al.
Publicado: (2026)
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning
por: Lyu, Xiaoting, et al.
Publicado: (2024)
por: Lyu, Xiaoting, et al.
Publicado: (2024)
POLAR: Policy-based Layerwise Reinforcement Learning Method for Stealthy Backdoor Attacks in Federated Learning
por: Yu, Kuai, et al.
Publicado: (2025)
por: Yu, Kuai, et al.
Publicado: (2025)
SDBA: A Stealthy and Long-Lasting Durable Backdoor Attack in Federated Learning
por: Choe, Minyeong, et al.
Publicado: (2024)
por: Choe, Minyeong, et al.
Publicado: (2024)
Fairness-Constrained Optimization Attack in Federated Learning
por: Kasyap, Harsh, et al.
Publicado: (2025)
por: Kasyap, Harsh, et al.
Publicado: (2025)
Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models
por: Yang, Yuning, et al.
Publicado: (2025)
por: Yang, Yuning, et al.
Publicado: (2025)
Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits
por: Wang, Zhiwei, et al.
Publicado: (2024)
por: Wang, Zhiwei, et al.
Publicado: (2024)
Entropy-driven Fair and Effective Federated Learning
por: Wang, Lin, et al.
Publicado: (2023)
por: Wang, Lin, et al.
Publicado: (2023)
Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
por: Duan, Yuying, et al.
Publicado: (2024)
por: Duan, Yuying, et al.
Publicado: (2024)
Fair Anomaly Detection For Imbalanced Groups
por: Wu, Ziwei, et al.
Publicado: (2024)
por: Wu, Ziwei, et al.
Publicado: (2024)
Mistletoe: Stealthy Acceleration-Collapse Attacks on Speculative Decoding
por: Sun, Shuoyang, et al.
Publicado: (2026)
por: Sun, Shuoyang, et al.
Publicado: (2026)
AFed: Algorithmic Fair Federated Learning
por: Chen, Huiqiang, et al.
Publicado: (2025)
por: Chen, Huiqiang, et al.
Publicado: (2025)
Procedural Fairness in Machine Learning
por: Wang, Ziming, et al.
Publicado: (2024)
por: Wang, Ziming, et al.
Publicado: (2024)
Preserving AUC Fairness in Learning with Noisy Protected Groups
por: Wu, Mingyang, et al.
Publicado: (2025)
por: Wu, Mingyang, et al.
Publicado: (2025)
pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning
por: Lei, Haoyu, et al.
Publicado: (2025)
por: Lei, Haoyu, et al.
Publicado: (2025)
Equipping Federated Graph Neural Networks with Structure-aware Group Fairness
por: Cui, Nan, et al.
Publicado: (2023)
por: Cui, Nan, et al.
Publicado: (2023)
SilentStriker:Toward Stealthy Bit-Flip Attacks on Large Language Models
por: Xu, Haotian, et al.
Publicado: (2025)
por: Xu, Haotian, et al.
Publicado: (2025)
On Demographic Group Fairness Guarantees in Deep Learning
por: Luo, Yan, et al.
Publicado: (2024)
por: Luo, Yan, et al.
Publicado: (2024)
SUA: Stealthy Multimodal Large Language Model Unlearning Attack
por: Zhang, Xianren, et al.
Publicado: (2025)
por: Zhang, Xianren, et al.
Publicado: (2025)
Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
por: Wang, Ziming, et al.
Publicado: (2025)
por: Wang, Ziming, et al.
Publicado: (2025)
FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
por: Li, Minghan, et al.
Publicado: (2025)
por: Li, Minghan, et al.
Publicado: (2025)
Distribution-Free Fair Federated Learning with Small Samples
por: Yin, Qichuan, et al.
Publicado: (2024)
por: Yin, Qichuan, et al.
Publicado: (2024)
Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
por: Wang, Xiaobao, et al.
Publicado: (2025)
por: Wang, Xiaobao, et al.
Publicado: (2025)
Global Group Fairness in Federated Learning via Function Tracking
por: Rychener, Yves, et al.
Publicado: (2025)
por: Rychener, Yves, et al.
Publicado: (2025)
Controllable and Stealthy Shilling Attacks via Dispersive Latent Diffusion
por: Qiao, Shutong, et al.
Publicado: (2025)
por: Qiao, Shutong, et al.
Publicado: (2025)
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models
por: Xu, Yuancheng, et al.
Publicado: (2024)
por: Xu, Yuancheng, et al.
Publicado: (2024)
COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability
por: Guo, Xingang, et al.
Publicado: (2024)
por: Guo, Xingang, et al.
Publicado: (2024)
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
por: Zhang, Li, et al.
Publicado: (2025)
por: Zhang, Li, et al.
Publicado: (2025)
On the Fairness of Privacy Protection: Measuring and Mitigating the Disparity of Group Privacy Risks for Differentially Private Machine Learning
por: Yang, Zhi, et al.
Publicado: (2025)
por: Yang, Zhi, et al.
Publicado: (2025)
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
por: Lu, Yun, et al.
Publicado: (2026)
por: Lu, Yun, et al.
Publicado: (2026)
Fairness May Backfire: When Leveling-Down Occurs in Fair Machine Learning
por: Yang, Yi, et al.
Publicado: (2026)
por: Yang, Yi, et al.
Publicado: (2026)
BAPFL: Exploring Backdoor Attacks Against Prototype-based Federated Learning
por: Zeng, Honghong, et al.
Publicado: (2025)
por: Zeng, Honghong, et al.
Publicado: (2025)
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
por: Zhu, Shengkun, et al.
Publicado: (2024)
por: Zhu, Shengkun, et al.
Publicado: (2024)
Fairness Regularization in Federated Learning
por: Kharaghani, Zahra, et al.
Publicado: (2025)
por: Kharaghani, Zahra, et al.
Publicado: (2025)
Efficient and Robust Regularized Federated Recommendation
por: Liu, Langming, et al.
Publicado: (2024)
por: Liu, Langming, et al.
Publicado: (2024)
AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
por: Huang, Yinghui, et al.
Publicado: (2024)
por: Huang, Yinghui, et al.
Publicado: (2024)
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks
por: Zhao, Pu, et al.
Publicado: (2019)
por: Zhao, Pu, et al.
Publicado: (2019)
Fair Conformal Classification via Learning Representation-Based Groups
por: Xu, Senrong, et al.
Publicado: (2026)
por: Xu, Senrong, et al.
Publicado: (2026)
BadFair: Backdoored Fairness Attacks with Group-conditioned Triggers
por: Xue, Jiaqi, et al.
Publicado: (2024)
por: Xue, Jiaqi, et al.
Publicado: (2024)
Ejemplares similares
-
Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect
por: Gao, Jiashi, et al.
Publicado: (2023) -
Stealthy Poisoning Attacks Bypass Defenses in Regression Settings
por: Carnerero-Cano, Javier, et al.
Publicado: (2026) -
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning
por: Lyu, Xiaoting, et al.
Publicado: (2024) -
POLAR: Policy-based Layerwise Reinforcement Learning Method for Stealthy Backdoor Attacks in Federated Learning
por: Yu, Kuai, et al.
Publicado: (2025) -
SDBA: A Stealthy and Long-Lasting Durable Backdoor Attack in Federated Learning
por: Choe, Minyeong, et al.
Publicado: (2024)