A Data-Driven Defense against Edge-case Model Poisoning Attacks on Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Purohit, Kiran, Das, Soumi, Bhattacharya, Sourangshu, Rana, Santu |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning
by: Sun, Shihua, et al.
Published: (2024)
by: Sun, Shihua, et al.
Published: (2024)
SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning Attacks
by: Paracha, Anum, et al.
Published: (2025)
by: Paracha, Anum, et al.
Published: (2025)
SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
by: Zhang, Heyi, et al.
Published: (2025)
by: Zhang, Heyi, et al.
Published: (2025)
Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
by: Obioma, Chibueze Peace, et al.
Published: (2025)
by: Obioma, Chibueze Peace, et al.
Published: (2025)
Sybil-based Virtual Data Poisoning Attacks in Federated Learning
by: Zhu, Changxun, et al.
Published: (2025)
by: Zhu, Changxun, et al.
Published: (2025)
Poisoning Attacks and Defenses to Federated Unlearning
by: Wang, Wenbin, et al.
Published: (2025)
by: Wang, Wenbin, et al.
Published: (2025)
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning
by: Campos, Enrique Mármol, et al.
Published: (2024)
by: Campos, Enrique Mármol, et al.
Published: (2024)
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)
by: Han, Sungwon, et al.
Published: (2024)
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning
by: Luo, Xinjian, et al.
Published: (2020)
by: Luo, Xinjian, et al.
Published: (2020)
GShield: Mitigating Poisoning Attacks in Federated Learning
by: M., Sameera K., et al.
Published: (2025)
by: M., Sameera K., et al.
Published: (2025)
FedRecAttack: Model Poisoning Attack to Federated Recommendation
by: Rong, Dazhong, et al.
Published: (2022)
by: Rong, Dazhong, et al.
Published: (2022)
Local Environment Poisoning Attacks on Federated Reinforcement Learning
by: Ma, Evelyn, et al.
Published: (2023)
by: Ma, Evelyn, et al.
Published: (2023)
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV
by: Talpur, Anum, et al.
Published: (2022)
by: Talpur, Anum, et al.
Published: (2022)
Decaf: Data Distribution Decompose Attack against Federated Learning
by: Dai, Zhiyang, et al.
Published: (2024)
by: Dai, Zhiyang, et al.
Published: (2024)
Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning
by: Jiang, Yu, et al.
Published: (2024)
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)
by: Raza, Ali, et al.
Published: (2022)
Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning
by: Mo, Wenjin, et al.
Published: (2025)
by: Mo, Wenjin, et al.
Published: (2025)
Sparsification Under Siege: Dual-Level Defense Against Poisoning in Communication-Efficient Federated Learning
by: Jin, Zhiyong, et al.
Published: (2025)
by: Jin, Zhiyong, et al.
Published: (2025)
EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
by: Zhang, Tianyun, et al.
Published: (2026)
by: Zhang, Tianyun, et al.
Published: (2026)
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks
by: Liu, Shijie, et al.
Published: (2023)
by: Liu, Shijie, et al.
Published: (2023)
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
by: Fan, Mingyuan, et al.
Published: (2022)
by: Fan, Mingyuan, et al.
Published: (2022)
How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution
by: Wang, Jinbo, et al.
Published: (2024)
by: Wang, Jinbo, et al.
Published: (2024)
Provable Watermarking for Data Poisoning Attacks
by: Zhu, Yifan, et al.
Published: (2025)
by: Zhu, Yifan, et al.
Published: (2025)
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning
by: Zhang, Yiwei, et al.
Published: (2024)
by: Zhang, Yiwei, et al.
Published: (2024)
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
by: Wang, Yujing, et al.
Published: (2024)
by: Wang, Yujing, et al.
Published: (2024)
Stealthy Poisoning Attacks Bypass Defenses in Regression Settings
by: Carnerero-Cano, Javier, et al.
Published: (2026)
by: Carnerero-Cano, Javier, et al.
Published: (2026)
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
by: Fereidooni, Hossein, et al.
Published: (2023)
by: Fereidooni, Hossein, et al.
Published: (2023)
Indiscriminate Data Poisoning Attacks on Neural Networks
by: Lu, Yiwei, et al.
Published: (2022)
by: Lu, Yiwei, et al.
Published: (2022)
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
by: Xu, Zhangchen, et al.
Published: (2024)
by: Xu, Zhangchen, et al.
Published: (2024)
Poison with Style: A Practical Poisoning Attack on Code Large Language Models
by: Tran, Khang, et al.
Published: (2026)
by: Tran, Khang, et al.
Published: (2026)
PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models
by: Panaitescu-Liess, Michael-Andrei, et al.
Published: (2025)
by: Panaitescu-Liess, Michael-Andrei, et al.
Published: (2025)
KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
by: Arazzi, Marco, et al.
Published: (2024)
by: Arazzi, Marco, et al.
Published: (2024)
From Theory to Practice: Evaluating Data Poisoning Attacks and Defenses in In-Context Learning on Social Media Health Discourse
by: Jhuma, Rabeya Amin, et al.
Published: (2025)
by: Jhuma, Rabeya Amin, et al.
Published: (2025)
Data Poisoning Attacks in Intelligent Transportation Systems: A Survey
by: Wang, Feilong, et al.
Published: (2024)
by: Wang, Feilong, et al.
Published: (2024)
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
by: Singampalli, Akhil, et al.
Published: (2024)
by: Singampalli, Akhil, et al.
Published: (2024)
Inverting Gradient Attacks Makes Powerful Data Poisoning
by: Bouaziz, Wassim, et al.
Published: (2024)
by: Bouaziz, Wassim, et al.
Published: (2024)
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
by: He, Longzhu, et al.
Published: (2025)
by: He, Longzhu, et al.
Published: (2025)
Attack and Defense of Deep Learning Models in the Field of Web Attack Detection
by: Shi, Lijia, et al.
Published: (2024)
by: Shi, Lijia, et al.
Published: (2024)
Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations
by: Li, Jiate, et al.
Published: (2025)
by: Li, Jiate, et al.
Published: (2025)
Semantic Chameleon: Corpus-Dependent Poisoning Attacks and Defenses in RAG Systems
by: Thornton, Scott
Published: (2026)
by: Thornton, Scott
Published: (2026)
Similar Items
-
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning
by: Sun, Shihua, et al.
Published: (2024) -
SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning Attacks
by: Paracha, Anum, et al.
Published: (2025) -
SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
by: Zhang, Heyi, et al.
Published: (2025) -
Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
by: Obioma, Chibueze Peace, et al.
Published: (2025) -
Sybil-based Virtual Data Poisoning Attacks in Federated Learning
by: Zhu, Changxun, et al.
Published: (2025)