Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
Fuente:
arXiv
Guardado en:
| Autores principales: | Schneider, Thomas, Suresh, Ajith, Yalame, Hossein |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
WW-FL: Secure and Private Large-Scale Federated Learning
por: Marx, Felix, et al.
Publicado: (2023)
por: Marx, Felix, et al.
Publicado: (2023)
Attesting Distributional Properties of Training Data for Machine Learning
por: Duddu, Vasisht, et al.
Publicado: (2023)
por: Duddu, Vasisht, et al.
Publicado: (2023)
ScionFL: Efficient and Robust Secure Quantized Aggregation
por: Ben-Itzhak, Yaniv, et al.
Publicado: (2022)
por: Ben-Itzhak, Yaniv, et al.
Publicado: (2022)
On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy
por: Wang, Zijian, et al.
Publicado: (2025)
por: Wang, Zijian, et al.
Publicado: (2025)
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
por: Wang, Yujing, et al.
Publicado: (2024)
por: Wang, Yujing, et al.
Publicado: (2024)
Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
por: Zhao, Wenwei, et al.
Publicado: (2026)
por: Zhao, Wenwei, et al.
Publicado: (2026)
Sparsification Under Siege: Dual-Level Defense Against Poisoning in Communication-Efficient Federated Learning
por: Jin, Zhiyong, et al.
Publicado: (2025)
por: Jin, Zhiyong, et al.
Publicado: (2025)
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
por: Fereidooni, Hossein, et al.
Publicado: (2023)
por: Fereidooni, Hossein, et al.
Publicado: (2023)
How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution
por: Wang, Jinbo, et al.
Publicado: (2024)
por: Wang, Jinbo, et al.
Publicado: (2024)
High-Throughput Secure Multiparty Computation with an Honest Majority in Various Network Settings
por: Harth-Kitzerow, Christopher, et al.
Publicado: (2022)
por: Harth-Kitzerow, Christopher, et al.
Publicado: (2022)
GShield: Mitigating Poisoning Attacks in Federated Learning
por: M., Sameera K., et al.
Publicado: (2025)
por: M., Sameera K., et al.
Publicado: (2025)
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
por: Wen, Yuxin, et al.
Publicado: (2024)
por: Wen, Yuxin, et al.
Publicado: (2024)
Poisoning the Watchtower: Prompt Injection Attacks Against LLM-Augmented Security Operations Through Adversarial Log Content
por: Pandey, Rohan, et al.
Publicado: (2026)
por: Pandey, Rohan, et al.
Publicado: (2026)
Local Environment Poisoning Attacks on Federated Reinforcement Learning
por: Ma, Evelyn, et al.
Publicado: (2023)
por: Ma, Evelyn, et al.
Publicado: (2023)
A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption
por: Nguyen, Khoa, et al.
Publicado: (2025)
por: Nguyen, Khoa, et al.
Publicado: (2025)
EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
por: Zhang, Tianyun, et al.
Publicado: (2026)
por: Zhang, Tianyun, et al.
Publicado: (2026)
FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning
por: Han, Sungwon, et al.
Publicado: (2024)
por: Han, Sungwon, et al.
Publicado: (2024)
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents
por: Rathbun, Ethan, et al.
Publicado: (2024)
por: Rathbun, Ethan, et al.
Publicado: (2024)
Online Poisoning Attack Against Reinforcement Learning under Black-box Environments
por: Li, Jianhui, et al.
Publicado: (2024)
por: Li, Jianhui, et al.
Publicado: (2024)
Sybil-based Virtual Data Poisoning Attacks in Federated Learning
por: Zhu, Changxun, et al.
Publicado: (2025)
por: Zhu, Changxun, et al.
Publicado: (2025)
DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization
por: Luo, Xiaoyu, et al.
Publicado: (2024)
por: Luo, Xiaoyu, et al.
Publicado: (2024)
SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning Attacks
por: Paracha, Anum, et al.
Publicado: (2025)
por: Paracha, Anum, et al.
Publicado: (2025)
Privacy Against Agnostic Inference Attacks in Vertical Federated Learning
por: Varasteh, Morteza
Publicado: (2023)
por: Varasteh, Morteza
Publicado: (2023)
Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection
por: Abadi, Aydin, et al.
Publicado: (2024)
por: Abadi, Aydin, et al.
Publicado: (2024)
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning
por: Sun, Shihua, et al.
Publicado: (2024)
por: Sun, Shihua, et al.
Publicado: (2024)
Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning
por: Jiang, Yu, et al.
Publicado: (2024)
por: Jiang, Yu, et al.
Publicado: (2024)
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
por: Raza, Ali, et al.
Publicado: (2022)
por: Raza, Ali, et al.
Publicado: (2022)
Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes
por: Hamidi, Shayan Mohajer, et al.
Publicado: (2024)
por: Hamidi, Shayan Mohajer, et al.
Publicado: (2024)
Exact Certification of (Graph) Neural Networks Against Label Poisoning
por: Sabanayagam, Mahalakshmi, et al.
Publicado: (2024)
por: Sabanayagam, Mahalakshmi, et al.
Publicado: (2024)
Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
por: Dong, Wenhan, et al.
Publicado: (2024)
por: Dong, Wenhan, et al.
Publicado: (2024)
Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data
por: Hsu, I-Jung, et al.
Publicado: (2025)
por: Hsu, I-Jung, et al.
Publicado: (2025)
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security
por: Ceviz, Ozlem, et al.
Publicado: (2023)
por: Ceviz, Ozlem, et al.
Publicado: (2023)
Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge
por: Bertoli, Gustavo de Carvalho
Publicado: (2026)
por: Bertoli, Gustavo de Carvalho
Publicado: (2026)
On the Efficiency of Privacy Attacks in Federated Learning
por: Tabassum, Nawrin, et al.
Publicado: (2024)
por: Tabassum, Nawrin, et al.
Publicado: (2024)
Preserving Privacy and Security in Federated Learning
por: Nguyen, Truc, et al.
Publicado: (2022)
por: Nguyen, Truc, et al.
Publicado: (2022)
A Systematic Review of Poisoning Attacks Against Large Language Models
por: Fendley, Neil, et al.
Publicado: (2025)
por: Fendley, Neil, et al.
Publicado: (2025)
DeepLeak: Privacy Enhancing Hardening of Model Explanations Against Membership Leakage
por: Hmida, Firas Ben, et al.
Publicado: (2026)
por: Hmida, Firas Ben, et al.
Publicado: (2026)
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
por: Gosch, Lukas, et al.
Publicado: (2024)
por: Gosch, Lukas, et al.
Publicado: (2024)
Defending Against Poisoning Attacks in Federated Learning with Blockchain
por: Dong, Nanqing, et al.
Publicado: (2023)
por: Dong, Nanqing, et al.
Publicado: (2023)
Survey of Privacy Threats and Countermeasures in Federated Learning
por: Hayashitani, Masahiro, et al.
Publicado: (2024)
por: Hayashitani, Masahiro, et al.
Publicado: (2024)
Ejemplares similares
-
WW-FL: Secure and Private Large-Scale Federated Learning
por: Marx, Felix, et al.
Publicado: (2023) -
Attesting Distributional Properties of Training Data for Machine Learning
por: Duddu, Vasisht, et al.
Publicado: (2023) -
ScionFL: Efficient and Robust Secure Quantized Aggregation
por: Ben-Itzhak, Yaniv, et al.
Publicado: (2022) -
On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy
por: Wang, Zijian, et al.
Publicado: (2025) -
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
por: Wang, Yujing, et al.
Publicado: (2024)