Buchumschlag

Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Li, Bin, Miao, Xiaoye, Zhang, Yan, Yin, Jianwei
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Machine Learning
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Lossless Privacy-Preserving Aggregation for Decentralized Federated Learning
von: Miao, Xiaoye, et al.
Veröffentlicht: (2025)

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks
von: Campos, Enrique Mármol, et al.
Veröffentlicht: (2025)

Poisoning Attacks and Defenses to Federated Unlearning
von: Wang, Wenbin, et al.
Veröffentlicht: (2025)

Adversarial Training for Defense Against Label Poisoning Attacks
von: Bal, Melis Ilayda, et al.
Veröffentlicht: (2025)

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
von: Zhang, Heyi, et al.
Veröffentlicht: (2025)

SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning Attacks
von: Paracha, Anum, et al.
Veröffentlicht: (2025)

DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences
von: Feng, Chao, et al.
Veröffentlicht: (2025)

A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder
von: Zheng, Jingjing, et al.
Veröffentlicht: (2024)

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning
von: Liu, Shijie, et al.
Veröffentlicht: (2025)

Sparsification Under Siege: Dual-Level Defense Against Poisoning in Communication-Efficient Federated Learning
von: Jin, Zhiyong, et al.
Veröffentlicht: (2025)

Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
von: Wang, Yujing, et al.
Veröffentlicht: (2024)

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning
von: Mo, Wenjin, et al.
Veröffentlicht: (2025)

Defending Against Poisoning Attacks in Federated Learning with Blockchain
von: Dong, Nanqing, et al.
Veröffentlicht: (2023)

Optimal Defenses Against Gradient Reconstruction Attacks
von: Chen, Yuxiao, et al.
Veröffentlicht: (2024)

FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning
von: Han, Sungwon, et al.
Veröffentlicht: (2024)

A Data-Driven Defense against Edge-case Model Poisoning Attacks on Federated Learning
von: Purohit, Kiran, et al.
Veröffentlicht: (2023)

Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration
von: Li, Qinglun, et al.
Veröffentlicht: (2024)

SpectralKrum: A Spectral-Geometric Defense Against Byzantine Attacks in Federated Learning
von: Tripathi, Aditya, et al.
Veröffentlicht: (2025)

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution
von: Wang, Jinbo, et al.
Veröffentlicht: (2024)

Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning
von: Assumpcao, Nicolas Riccieri Gardin, et al.
Veröffentlicht: (2025)

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments
von: Li, Jianhui, et al.
Veröffentlicht: (2024)

A New Federated Learning Framework Against Gradient Inversion Attacks
von: Guo, Pengxin, et al.
Veröffentlicht: (2024)

A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
von: elShehaby, Mohamed, et al.
Veröffentlicht: (2026)

Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning
von: Nilsson, Ivo Osterberg, et al.
Veröffentlicht: (2026)

GShield: Mitigating Poisoning Attacks in Federated Learning
von: M., Sameera K., et al.
Veröffentlicht: (2025)

VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification
von: Cho, Yungi, et al.
Veröffentlicht: (2024)

KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
von: Arazzi, Marco, et al.
Veröffentlicht: (2024)

Logits Poisoning Attack in Federated Distillation
von: Tang, Yuhan, et al.
Veröffentlicht: (2024)

ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization
von: Gufran, Danish, et al.
Veröffentlicht: (2026)

Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning
von: Jiang, Yu, et al.
Veröffentlicht: (2024)

OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement
von: Li, Qinglun, et al.
Veröffentlicht: (2024)

Local Environment Poisoning Attacks on Federated Reinforcement Learning
von: Ma, Evelyn, et al.
Veröffentlicht: (2023)

Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
von: Schneider, Thomas, et al.
Veröffentlicht: (2024)

MaskPure: Improving Defense Against Text Adversaries with Stochastic Purification
von: Gietz, Harrison, et al.
Veröffentlicht: (2024)

Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learning
von: Ali, Hassan, et al.
Veröffentlicht: (2024)

Topology-Independent Robustness of the Weighted Mean under Label Poisoning Attacks in Heterogeneous Decentralized Learning
von: Peng, Jie, et al.
Veröffentlicht: (2026)

Stealthy Poisoning Attacks Bypass Defenses in Regression Settings
von: Carnerero-Cano, Javier, et al.
Veröffentlicht: (2026)

Robust Thompson Sampling Algorithms Against Reward Poisoning Attacks
von: Xu, Yinglun, et al.
Veröffentlicht: (2024)

Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning
von: Li, Jianwei, et al.
Veröffentlicht: (2023)

FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning
von: Liu, Sheng, et al.
Veröffentlicht: (2026)