Cover Image

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Jinbo, Wang, Ruijin, Zhang, Fengli
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
by: Qiu, Pengyu, et al.
Published: (2022)

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

Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend
by: Liu, Yige, et al.
Published: (2026)

A Systematic Review of Poisoning Attacks Against Large Language Models
by: Fendley, Neil, et al.
Published: (2025)

Defending Large Language Models Against Attacks With Residual Stream Activation Analysis
by: Kawasaki, Amelia, et al.
Published: (2024)

Invariant Aggregator for Defending against Federated Backdoor Attacks
by: Wang, Xiaoyang, et al.
Published: (2022)

FL-Defender: Combating Targeted Attacks in Federated Learning
by: Jebreel, Najeeb, et al.
Published: (2022)

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

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

Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning
by: Sun, Shihua, et al.
Published: (2024)

Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks
by: Halloran, John T., et al.
Published: (2026)

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

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

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

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

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

Poison with Style: A Practical Poisoning Attack on Code Large Language Models
by: Tran, Khang, et al.
Published: (2026)

Privacy Against Agnostic Inference Attacks in Vertical Federated Learning
by: Varasteh, Morteza
Published: (2023)

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

SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents
by: Rathbun, Ethan, et al.
Published: (2024)

Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learning
by: Liu, Yige, et al.
Published: (2026)

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

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

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

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

Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
by: Tan, Qi, et al.
Published: (2024)

Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)
by: Ebron, Sheldon C., et al.
Published: (2023)

PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
by: Zou, Wei, et al.
Published: (2024)

Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning
by: Lee, Seohyun, et al.
Published: (2025)

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

MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training
by: Li, Jiacheng, et al.
Published: (2023)

EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
by: Zhang, Tianyun, et al.
Published: (2026)

Defending Against Indirect Prompt Injection Attacks With Spotlighting
by: Hines, Keegan, et al.
Published: (2024)

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

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

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

Data Poisoning Attacks in Intelligent Transportation Systems: A Survey
by: Wang, Feilong, et al.
Published: (2024)

Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
by: Gosch, Lukas, et al.
Published: (2024)

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