Image de couverture de livre

Fully Decentralized Certified Unlearning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Lamri, Hithem, Maniatakos, Michail
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

ReVeil: Unconstrained Concealed Backdoor Attack on Deep Neural Networks using Machine Unlearning
par: Alam, Manaar, et autres
Publié: (2025)

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems
par: Lamri, Hithem, et autres
Publié: (2025)

LLMPot: Dynamically Configured LLM-based Honeypot for Industrial Protocol and Physical Process Emulation
par: Vasilatos, Christoforos, et autres
Publié: (2024)

Certified Unlearning in Decentralized Federated Learning
par: Wu, Hengliang, et autres
Publié: (2026)

CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations
par: Sefsaf, Bilel, et autres
Publié: (2026)

BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneity
par: Guerra-Manzanares, Alejandro, et autres
Publié: (2025)

Certified Signed Graph Unlearning
par: Zhao, Junpeng, et autres
Publié: (2025)

Certified Unlearning for Neural Networks
par: Koloskova, Anastasia, et autres
Publié: (2025)

Towards Certified Unlearning for Deep Neural Networks
par: Zhang, Binchi, et autres
Publié: (2024)

Hessian-Free Online Certified Unlearning
par: Qiao, Xinbao, et autres
Publié: (2024)

Privacy-preserving machine learning for healthcare: open challenges and future perspectives
par: Guerra-Manzanares, Alejandro, et autres
Publié: (2023)

Certified Minimax Unlearning with Generalization Rates and Deletion Capacity
par: Liu, Jiaqi, et autres
Publié: (2023)

Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions
par: Mu, Siqiao, et autres
Publié: (2024)

PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks
par: Wang, Yue, et autres
Publié: (2022)

Scalable and Certifiable Graph Unlearning: Overcoming the Approximation Error Barrier
par: Yi, Lu, et autres
Publié: (2024)

Gaussian Certified Unlearning in High Dimensions: A Hypothesis Testing Approach
par: Pandey, Aaradhya, et autres
Publié: (2025)

Sequential Subspace Noise Injection Prevents Accuracy Collapse in Certified Unlearning
par: Dolgova, Polina, et autres
Publié: (2026)

IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
par: Dong, Yushun, et autres
Publié: (2024)

A Certified Unlearning Approach without Access to Source Data
par: Basaran, Umit Yigit, et autres
Publié: (2025)

Certified Machine Unlearning via Noisy Stochastic Gradient Descent
par: Chien, Eli, et autres
Publié: (2024)

Forgettable Federated Linear Learning with Certified Data Unlearning
par: Jin, Ruinan, et autres
Publié: (2023)

Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data
par: Iofinova, Eugenia, et autres
Publié: (2026)

Beyond Uniform Deletion: A Data Value-Weighted Framework for Certified Machine Unlearning
par: He, Lisong, et autres
Publié: (2025)

A Robust Certified Machine Unlearning Method Under Distribution Shift
par: Guo, Jinduo, et autres
Publié: (2026)

Fully-Decentralized MADDPG with Networked Agents
par: Bolliger, Diego, et autres
Publié: (2025)

PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks
par: Li, Xuran, et autres
Publié: (2025)

Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
par: Jiang, Yu, et autres
Publié: (2025)

Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated Learning
par: Wang, Linian, et autres
Publié: (2025)

Fully Decentralized Cooperative Multi-Agent Reinforcement Learning is A Context Modeling Problem
par: Li, Chao, et autres
Publié: (2025)

Uncover and Unlearn Nuisances: Agnostic Fully Test-Time Adaptation
par: Srey, Ponhvoan, et autres
Publié: (2025)

Vanishing Variance Problem in Fully Decentralized Neural-Network Systems
par: Tian, Yongding, et autres
Publié: (2024)

Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
par: Jiang, Jiechuan, et autres
Publié: (2024)

Targeted Unlearning with Single Layer Unlearning Gradient
par: Cai, Zikui, et autres
Publié: (2024)

Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning
par: Zhang, Yubo, et autres
Publié: (2025)

Chisme: Fully Decentralized Differentiated Deep Learning for IoT Intelligence
par: Kuttivelil, Harikrishna, et autres
Publié: (2025)

Conformal Unlearning: A New Paradigm for Unlearning in Conformal Predictors
par: Alkhatib, Yahya, et autres
Publié: (2025)

Erase or Hide? Suppressing Spurious Unlearning Neurons for Robust Unlearning
par: Yang, Nakyeong, et autres
Publié: (2025)

FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning
par: Li, Zitong, et autres
Publié: (2025)

An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations
par: Gao, Yichen, et autres
Publié: (2026)

Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers
par: Cha, Sungmin, et autres
Publié: (2023)