FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attacks
Fuente:
arXiv
Guardado en:
| Autores principales: | Romandini, Nicolò, Borcea, Cristian, Montanari, Rebecca, Foschini, Luca |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics
por: Romandini, Nicolò, et al.
Publicado: (2024)
por: Romandini, Nicolò, et al.
Publicado: (2024)
FedRecAttack: Model Poisoning Attack to Federated Recommendation
por: Rong, Dazhong, et al.
Publicado: (2022)
por: Rong, Dazhong, et al.
Publicado: (2022)
Poisoning Attacks and Defenses to Federated Unlearning
por: Wang, Wenbin, et al.
Publicado: (2025)
por: Wang, Wenbin, et al.
Publicado: (2025)
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning
por: Lai, Phung, et al.
Publicado: (2025)
por: Lai, Phung, et al.
Publicado: (2025)
Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-Gradients
por: Mora, Alessio, et al.
Publicado: (2025)
por: Mora, Alessio, et al.
Publicado: (2025)
AdFL: In-Browser Federated Learning for Online Advertisement
por: Alemari, Ahmad, et al.
Publicado: (2026)
por: Alemari, Ahmad, et al.
Publicado: (2026)
Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
por: Zhao, Wenwei, et al.
Publicado: (2026)
por: Zhao, Wenwei, et al.
Publicado: (2026)
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
por: Di, Jimmy Z., et al.
Publicado: (2022)
por: Di, Jimmy Z., et al.
Publicado: (2022)
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)
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning
por: Campos, Enrique Mármol, et al.
Publicado: (2024)
por: Campos, Enrique Mármol, et al.
Publicado: (2024)
Logits Poisoning Attack in Federated Distillation
por: Tang, Yuhan, et al.
Publicado: (2024)
por: Tang, Yuhan, et al.
Publicado: (2024)
Unlearning Backdoor Attacks through Gradient-Based Model Pruning
por: Dunnett, Kealan, et al.
Publicado: (2024)
por: Dunnett, Kealan, et al.
Publicado: (2024)
FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness
por: Wen, Siyuan, et al.
Publicado: (2025)
por: Wen, Siyuan, et al.
Publicado: (2025)
FedSpaLLM: Federated Pruning of Large Language Models
por: Bai, Guangji, et al.
Publicado: (2024)
por: Bai, Guangji, et al.
Publicado: (2024)
Potion: Towards Poison Unlearning
por: Schoepf, Stefan, et al.
Publicado: (2024)
por: Schoepf, Stefan, et al.
Publicado: (2024)
FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning
por: Herzog, Alexander, et al.
Publicado: (2024)
por: Herzog, Alexander, 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)
Machine Unlearning Fails to Remove Data Poisoning Attacks
por: Pawelczyk, Martin, et al.
Publicado: (2024)
por: Pawelczyk, Martin, 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)
FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning
por: Liu, Sheng, et al.
Publicado: (2026)
por: Liu, Sheng, et al.
Publicado: (2026)
CryptGNN: Enabling Secure Inference for Graph Neural Networks
por: Sen, Pritam, et al.
Publicado: (2025)
por: Sen, Pritam, et al.
Publicado: (2025)
SGFusion: Stochastic Geographic Gradient Fusion in Federated Learning
por: Nguyen, Khoa, et al.
Publicado: (2025)
por: Nguyen, Khoa, et al.
Publicado: (2025)
GShield: Mitigating Poisoning Attacks in Federated Learning
por: M., Sameera K., et al.
Publicado: (2025)
por: M., Sameera K., et al.
Publicado: (2025)
Orthogonal Soft Pruning for Efficient Class Unlearning
por: Gong, Qinghui, et al.
Publicado: (2025)
por: Gong, Qinghui, et al.
Publicado: (2025)
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences
por: Feng, Chao, et al.
Publicado: (2025)
por: Feng, Chao, et al.
Publicado: (2025)
FedLAD: A Linear Algebra Based Data Poisoning Defence for Federated Learning
por: Xiong, Qi, et al.
Publicado: (2025)
por: Xiong, Qi, 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)
Local Environment Poisoning Attacks on Federated Reinforcement Learning
por: Ma, Evelyn, et al.
Publicado: (2023)
por: Ma, Evelyn, et al.
Publicado: (2023)
Peak-Controlled Logits Poisoning Attack in Federated Distillation
por: Tang, Yuhan, et al.
Publicado: (2024)
por: Tang, Yuhan, et al.
Publicado: (2024)
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
por: Fraboni, Yann, et al.
Publicado: (2022)
por: Fraboni, Yann, et al.
Publicado: (2022)
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)
Sky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning
por: Zaman, Md Mahabub Uz, et al.
Publicado: (2025)
por: Zaman, Md Mahabub Uz, et al.
Publicado: (2025)
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning
por: Li, Bin, et al.
Publicado: (2025)
por: Li, Bin, et al.
Publicado: (2025)
Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics
por: Tran, Khang, et al.
Publicado: (2026)
por: Tran, Khang, et al.
Publicado: (2026)
FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
por: Huang, Hong, et al.
Publicado: (2025)
por: Huang, Hong, et al.
Publicado: (2025)
FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery
por: Li, Yue, et al.
Publicado: (2026)
por: Li, Yue, et al.
Publicado: (2026)
FedMef: Towards Memory-efficient Federated Dynamic Pruning
por: Huang, Hong, et al.
Publicado: (2024)
por: Huang, Hong, et al.
Publicado: (2024)
FedReview: A Review Mechanism for Rejecting Poisoned Updates in Federated Learning
por: Zheng, Tianhang, et al.
Publicado: (2024)
por: Zheng, Tianhang, et al.
Publicado: (2024)
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
por: Yi, Kai, et al.
Publicado: (2024)
por: Yi, Kai, et al.
Publicado: (2024)
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
por: Zhang, Zifan, et al.
Publicado: (2024)
por: Zhang, Zifan, et al.
Publicado: (2024)
Ejemplares similares
-
Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics
por: Romandini, Nicolò, et al.
Publicado: (2024) -
FedRecAttack: Model Poisoning Attack to Federated Recommendation
por: Rong, Dazhong, et al.
Publicado: (2022) -
Poisoning Attacks and Defenses to Federated Unlearning
por: Wang, Wenbin, et al.
Publicado: (2025) -
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning
por: Lai, Phung, et al.
Publicado: (2025) -
Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-Gradients
por: Mora, Alessio, et al.
Publicado: (2025)