Cover Image

Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Heng, Zhu, Tianqing, Zhang, Lefeng, Zhou, Wanlei, Yu, Philip S.
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
by: Zuo, Xuhan, et al.
Published: (2024)

Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives
by: Wang, Linlin, et al.
Published: (2024)

Identify Backdoored Model in Federated Learning via Individual Unlearning
by: Xu, Jiahao, et al.
Published: (2024)

Upcycling Noise for Federated Unlearning
by: Chen, Jianan, et al.
Published: (2024)

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

EFU: Enforcing Federated Unlearning via Functional Encryption
by: Mohammadi, Samaneh, et al.
Published: (2025)

Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning
by: Gong, Zirui, et al.
Published: (2025)

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning
by: Hosain, Md. Tanzib, et al.
Published: (2025)

Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
by: Qin, Laiqiao, et al.
Published: (2024)

Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation
by: Xu, Jiahao, et al.
Published: (2024)

Beyond Denial-of-Service: The Puppeteer's Attack for Fine-Grained Control in Ranking-Based Federated Learning
by: Chen, Zhihao, et al.
Published: (2026)

Efficient Language Model Architectures for Differentially Private Federated Learning
by: Ro, Jae Hun, et al.
Published: (2024)

Differentially Private Online Federated Learning with Correlated Noise
by: Zhang, Jiaojiao, et al.
Published: (2024)

Random Client Selection on Contrastive Federated Learning for Tabular Data
by: Ginanjar, Achmad, et al.
Published: (2025)

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning
by: Zhao, Zhihui, et al.
Published: (2025)

DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
by: Liu, Yu, et al.
Published: (2024)

SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA
by: Liu, Jianmin, et al.
Published: (2025)

Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
by: Zhang, Hangtao, et al.
Published: (2023)

Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation
by: Alsulaimawi, Zahir
Published: (2024)

Federated Unlearning with Gradient Descent and Conflict Mitigation
by: Pan, Zibin, et al.
Published: (2024)

No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning
by: Xing, Zhibo, et al.
Published: (2024)

Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
by: Xu, Jiahao, et al.
Published: (2025)

On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learning
by: Ye, Haoxiang, et al.
Published: (2023)

PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks
by: Zhang, Hongliang, et al.
Published: (2025)

DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation
by: Xu, Jie, et al.
Published: (2024)

Age Aware Scheduling for Differentially-Private Federated Learning
by: Lin, Kuan-Yu, et al.
Published: (2024)

Towards Trustworthy Federated Learning
by: Basharat, Alina, et al.
Published: (2025)

A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning
by: Zhou, Anqi, et al.
Published: (2024)

Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
by: Zhang, Baolei, et al.
Published: (2025)

Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach
by: Talaei, Mahtab, et al.
Published: (2024)

Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregation
by: Talaei, Mahtab, et al.
Published: (2024)

Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
by: Li, Xi, et al.
Published: (2024)

Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
by: Malekmohammadi, Saber, et al.
Published: (2024)

Federated Analytics-Empowered Frequent Pattern Mining for Decentralized Web 3.0 Applications
by: Wang, Zibo, et al.
Published: (2024)

FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
by: Zhang, Jianqing, et al.
Published: (2024)

Byzantine-Robust Decentralized Federated Learning
by: Fang, Minghong, et al.
Published: (2024)

Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning
by: Xu, Zhangchen, et al.
Published: (2024)

Federated Graph Learning with Adaptive Importance-based Sampling
by: Li, Anran, et al.
Published: (2024)

TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems
by: Di Gennaro, Marco, et al.
Published: (2025)

FedBaF: Federated Learning Aggregation Biased by a Foundation Model
by: Park, Jong-Ik, et al.
Published: (2024)