Copertina

Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kumar, K Naveen, Mohan, C Krishna, Machiry, Aravind
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Cryptography and Security Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV
di: Talpur, Anum, et al.
Pubblicazione: (2022)

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

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

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

WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems
di: M., Sameera K., et al.
Pubblicazione: (2025)

SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
di: Singampalli, Akhil, et al.
Pubblicazione: (2024)

Speed Kills: Exploring Confused Deputy Attacks Through Edge AI Accelerators
di: Danduri, Datta Manikanta Sri Hari, et al.
Pubblicazione: (2026)

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

Turning Generative Models Degenerate: The Power of Data Poisoning Attacks
di: Jiang, Shuli, et al.
Pubblicazione: (2024)

CBPF: Filtering Poisoned Data Based on Composite Backdoor Attack
di: Xia, Hanfeng, et al.
Pubblicazione: (2024)

When and Where do Data Poisons Attack Textual Inversion?
di: Styborski, Jeremy, et al.
Pubblicazione: (2025)

Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response
di: Ozturk, Ozer, et al.
Pubblicazione: (2025)

Robustness Analysis of Machine Learning Models for IoT Intrusion Detection Under Data Poisoning Attacks
di: Wulnye, Fortunatus Aabangbio, et al.
Pubblicazione: (2026)

Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks
di: Cotroneo, Domenico, et al.
Pubblicazione: (2023)

Defending Against Beta Poisoning Attacks in Machine Learning Models
di: Gulciftci, Nilufer, et al.
Pubblicazione: (2025)

Poison to Detect: Detection of Targeted Overfitting in Federated Learning
di: Mestari, Soumia Zohra El, et al.
Pubblicazione: (2025)

ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
di: Xu, Zhangchen, et al.
Pubblicazione: (2024)

Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning
di: Zhang, Yujie, et al.
Pubblicazione: (2024)

Leverage Variational Graph Representation For Model Poisoning on Federated Learning
di: Li, Kai, et al.
Pubblicazione: (2024)

Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents
di: Zou, Wei, et al.
Pubblicazione: (2026)

RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation
di: Mai, Peihua, et al.
Pubblicazione: (2024)

EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
di: Meerza, Syed Irfan Ali, et al.
Pubblicazione: (2024)

Data Poisoning in Deep Learning: A Survey
di: Zhao, Pinlong, et al.
Pubblicazione: (2025)

LoopTrap: Termination Poisoning Attacks on LLM Agents
di: Xu, Huiyu, et al.
Pubblicazione: (2026)

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

Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications
di: Zhang, Quan, et al.
Pubblicazione: (2024)

PIDP-Attack: Combining Prompt Injection with Database Poisoning Attacks on Retrieval-Augmented Generation Systems
di: Wang, Haozhen, et al.
Pubblicazione: (2026)

Poisoning Attacks on Federated Learning for Autonomous Driving
di: Garg, Sonakshi, et al.
Pubblicazione: (2024)

Data Poisoning Attacks on Off-Policy Policy Evaluation Methods
di: Lobo, Elita, et al.
Pubblicazione: (2024)

Hidden in the Metadata: Stealth Poisoning Attacks on Multimodal Retrieval-Augmented Generation
di: Edemacu, Kennedy, et al.
Pubblicazione: (2026)

Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models
di: Shan, Shawn, et al.
Pubblicazione: (2023)

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation
di: Yang, Peiru, et al.
Pubblicazione: (2026)

Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection
di: Gabrielli, Edoardo, et al.
Pubblicazione: (2023)

Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning
di: Li, Jiaxin, et al.
Pubblicazione: (2024)

Is Your Prompt Poisoning Code? Defect Induction Rates and Security Mitigation Strategies
di: Wang, Bin, et al.
Pubblicazione: (2025)

BadSkill: Backdoor Attacks on Agent Skills via Model-in-Skill Poisoning
di: Tie, Guiyao, et al.
Pubblicazione: (2026)

One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems
di: Chang, Zhiyuan, et al.
Pubblicazione: (2025)

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

Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models
di: Xu, Yuancheng, et al.
Pubblicazione: (2024)