Buchumschlag

FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Han, Sungwon, Song, Hyeonho, Park, Sungwon, Cha, Meeyoung
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Machine Learning Cryptography and Security
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

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

FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
von: Han, Shanshan, et al.
Veröffentlicht: (2023)

ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data
von: Zhao, Haodong, et al.
Veröffentlicht: (2026)

A Learning-Based Attack Framework to Break SOTA Poisoning Defenses in Federated Learning
von: Yang, Yuxin, et al.
Veröffentlicht: (2024)

FedRecAttack: Model Poisoning Attack to Federated Recommendation
von: Rong, Dazhong, et al.
Veröffentlicht: (2022)

FedCC: Robust Federated Learning against Model Poisoning Attacks
von: Jeong, Hyejun, et al.
Veröffentlicht: (2022)

Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
von: Xu, Runhua, et al.
Veröffentlicht: (2025)

SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models
von: Afane, Mohamed, et al.
Veröffentlicht: (2025)

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

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

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

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
von: Carney, Jane, et al.
Veröffentlicht: (2025)

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

Defense Against Prompt Injection Attack by Leveraging Attack Techniques
von: Chen, Yulin, et al.
Veröffentlicht: (2024)

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

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)

FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
von: Fereidooni, Hossein, et al.
Veröffentlicht: (2023)

FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning
von: Campos, Enrique Mármol, et al.
Veröffentlicht: (2024)

Defending against Data Poisoning Attacks in Federated Learning via User Elimination
von: Galanis, Nick
Veröffentlicht: (2024)

AntiFLipper: A Secure and Efficient Defense Against Label-Flipping Attacks in Federated Learning
von: Rahman, Aashnan, et al.
Veröffentlicht: (2025)

PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems
von: Sun, Wei, et al.
Veröffentlicht: (2026)

Sharpness-Aware Data Poisoning Attack
von: He, Pengfei, et al.
Veröffentlicht: (2023)

Defending Against Neural Network Model Inversion Attacks via Data Poisoning
von: Zhou, Shuai, et al.
Veröffentlicht: (2024)

Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
von: Xie, Chulin, et al.
Veröffentlicht: (2022)

DarkFed: A Data-Free Backdoor Attack in Federated Learning
von: Li, Minghui, et al.
Veröffentlicht: (2024)

Sybil-based Virtual Data Poisoning Attacks in Federated Learning
von: Zhu, Changxun, et al.
Veröffentlicht: (2025)

Federated Learning: Attacks, Defenses, Opportunities, and Challenges
von: Shirvani, Ghazaleh, et al.
Veröffentlicht: (2024)

Defense against Poisoning Attacks under Shuffle-DP
von: Wang, Siyi, et al.
Veröffentlicht: (2026)

FedPoisonTTP: A Threat Model and Poisoning Attack for Federated Test-Time Personalization
von: Iftee, Md Akil Raihan, et al.
Veröffentlicht: (2025)

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

PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks
von: Zhang, Hongliang, et al.
Veröffentlicht: (2025)

FedReview: A Review Mechanism for Rejecting Poisoned Updates in Federated Learning
von: Zheng, Tianhang, et al.
Veröffentlicht: (2024)

Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
von: Raza, Ali, et al.
Veröffentlicht: (2022)

Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
von: Kumar, K Naveen, et al.
Veröffentlicht: (2024)

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

Logit Poisoning Attack in Distillation-based Federated Learning and its Countermeasures
von: Yu, Yonghao, et al.
Veröffentlicht: (2024)

Model Poisoning Attacks to Federated Learning via Multi-Round Consistency
von: Xie, Yueqi, et al.
Veröffentlicht: (2024)

Poisoning Attacks and Defenses in Recommender Systems: A Survey
von: Wang, Zongwei, et al.
Veröffentlicht: (2024)

E-SAGE: Explainability-based Defense Against Backdoor Attacks on Graph Neural Networks
von: Yuan, Dingqiang, et al.
Veröffentlicht: (2024)