Cover Image

Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Romandini, Nicolò, Mora, Alessio, Mazzocca, Carlo, Montanari, Rebecca, Bellavista, Paolo
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

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

SoK: Security and Privacy of AI Agents for Blockchain
by: Romandini, Nicolò, et al.
Published: (2025)

A Survey on Decentralized Identifiers and Verifiable Credentials
by: Mazzocca, Carlo, et al.
Published: (2024)

Compact and Selective Disclosure for Verifiable Credentials
by: Buldini, Alessandro, et al.
Published: (2025)

Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
by: Wang, Houzhe, et al.
Published: (2026)

Federated Unlearning for Human Activity Recognition
by: Chen, Kongyang, et al.
Published: (2024)

Goldfish: An Efficient Federated Unlearning Framework
by: Wang, Houzhe, et al.
Published: (2024)

Blockchain-enabled Trustworthy Federated Unlearning
by: Lin, Yijing, et al.
Published: (2024)

Efficient Federated Unlearning under Plausible Deniability
by: Varshney, Ayush K., et al.
Published: (2024)

ConDa: Fast Federated Unlearning with Contribution Dampening
by: Chundawat, Vikram S, et al.
Published: (2024)

Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
by: Jiang, Yu, et al.
Published: (2024)

Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement
by: Wang, Houzhe, et al.
Published: (2026)

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

FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attacks
by: Romandini, Nicolò, et al.
Published: (2025)

Machine Unlearning for Traditional Models and Large Language Models: A Short Survey
by: Xu, Yi
Published: (2024)

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
by: Lu, Bingguang, et al.
Published: (2025)

A Robust Certified Machine Unlearning Method Under Distribution Shift
by: Guo, Jinduo, et al.
Published: (2026)

MUBox: A Critical Evaluation Framework of Deep Machine Unlearning
by: Li, Xiang, et al.
Published: (2025)

Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce
by: Brodzinski, Carl E. J.
Published: (2024)

Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
by: Lee, Hong kyu, et al.
Published: (2024)

Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue
by: Brunner, David, et al.
Published: (2024)

Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
by: Li, Na, et al.
Published: (2024)

Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
by: Jiang, Yu, et al.
Published: (2025)

Reinforcement Unlearning
by: Ye, Dayong, et al.
Published: (2023)

Classification-Head Bias in Class-Level Machine Unlearning: Diagnosis, Mitigation, and Evaluation
by: Zheng, Weidong, et al.
Published: (2026)

Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2026)

Adversarial Machine Unlearning
by: Di, Zonglin, et al.
Published: (2024)

Verifiable Unlearning on Edge
by: Maheri, Mohammad M, et al.
Published: (2025)

Edge Unlearning is Not "on Edge"! An Adaptive Exact Unlearning System on Resource-Constrained Devices
by: Xia, Xiaoyu, et al.
Published: (2024)

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
by: Inane, Ahmed Mehdi, et al.
Published: (2026)

Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy
by: Hayes, Jamie, et al.
Published: (2024)

Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey
by: Ye, Mang, et al.
Published: (2024)

Survey of Privacy Threats and Countermeasures in Federated Learning
by: Hayashitani, Masahiro, et al.
Published: (2024)

Scalable Federated Unlearning via Isolated and Coded Sharding
by: Lin, Yijing, et al.
Published: (2024)

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness
by: Wang, Cheng-Long, et al.
Published: (2024)

Verification of Machine Unlearning is Fragile
by: Zhang, Binchi, et al.
Published: (2024)

Certified Unlearning for Neural Networks
by: Koloskova, Anastasia, et al.
Published: (2025)

Efficient Unlearning with Privacy Guarantees
by: Domingo-Ferrer, Josep, et al.
Published: (2025)

Graph Unlearning with Efficient Partial Retraining
by: Zhang, Jiahao, et al.
Published: (2024)

On Large Language Model Continual Unlearning
by: Gao, Chongyang, et al.
Published: (2024)