Cover Image

FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attacks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Romandini, Nicolò, Borcea, Cristian, Montanari, Rebecca, Foschini, Luca
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics
by: Romandini, Nicolò, et al.
Published: (2024)

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

Poisoning Attacks and Defenses to Federated Unlearning
by: Wang, Wenbin, et al.
Published: (2025)

FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning
by: Lai, Phung, et al.
Published: (2025)

Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-Gradients
by: Mora, Alessio, et al.
Published: (2025)

AdFL: In-Browser Federated Learning for Online Advertisement
by: Alemari, Ahmad, et al.
Published: (2026)

Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
by: Zhao, Wenwei, et al.
Published: (2026)

Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
by: Di, Jimmy Z., et al.
Published: (2022)

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

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

Logits Poisoning Attack in Federated Distillation
by: Tang, Yuhan, et al.
Published: (2024)

Unlearning Backdoor Attacks through Gradient-Based Model Pruning
by: Dunnett, Kealan, et al.
Published: (2024)

FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness
by: Wen, Siyuan, et al.
Published: (2025)

FedSpaLLM: Federated Pruning of Large Language Models
by: Bai, Guangji, et al.
Published: (2024)

Potion: Towards Poison Unlearning
by: Schoepf, Stefan, et al.
Published: (2024)

FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning
by: Herzog, Alexander, et al.
Published: (2024)

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

Machine Unlearning Fails to Remove Data Poisoning Attacks
by: Pawelczyk, Martin, et al.
Published: (2024)

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

FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning
by: Liu, Sheng, et al.
Published: (2026)

CryptGNN: Enabling Secure Inference for Graph Neural Networks
by: Sen, Pritam, et al.
Published: (2025)

SGFusion: Stochastic Geographic Gradient Fusion in Federated Learning
by: Nguyen, Khoa, et al.
Published: (2025)

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

Orthogonal Soft Pruning for Efficient Class Unlearning
by: Gong, Qinghui, et al.
Published: (2025)

DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences
by: Feng, Chao, et al.
Published: (2025)

FedLAD: A Linear Algebra Based Data Poisoning Defence for Federated Learning
by: Xiong, Qi, et al.
Published: (2025)

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

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

Peak-Controlled Logits Poisoning Attack in Federated Distillation
by: Tang, Yuhan, et al.
Published: (2024)

SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
by: Fraboni, Yann, et al.
Published: (2022)

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)

Sky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning
by: Zaman, Md Mahabub Uz, et al.
Published: (2025)

Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning
by: Li, Bin, et al.
Published: (2025)

Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics
by: Tran, Khang, et al.
Published: (2026)

FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
by: Huang, Hong, et al.
Published: (2025)

FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery
by: Li, Yue, et al.
Published: (2026)

FedMef: Towards Memory-efficient Federated Dynamic Pruning
by: Huang, Hong, et al.
Published: (2024)

FedReview: A Review Mechanism for Rejecting Poisoned Updates in Federated Learning
by: Zheng, Tianhang, et al.
Published: (2024)

FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
by: Yi, Kai, et al.
Published: (2024)

Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
by: Zhang, Zifan, et al.
Published: (2024)