Identify Backdoored Model in Federated Learning via Individual Unlearning
Fuente:
arXiv
Saved in:
| Main Authors: | Xu, Jiahao, Zhang, Zikai, Hu, Rui |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
by: Xu, Jiahao, et al.
Published: (2025)
by: Xu, Jiahao, et al.
Published: (2025)
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation
by: Xu, Jiahao, et al.
Published: (2024)
by: Xu, Jiahao, et al.
Published: (2024)
Upcycling Noise for Federated Unlearning
by: Chen, Jianan, et al.
Published: (2024)
by: Chen, Jianan, et al.
Published: (2024)
Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
by: Li, Xi, et al.
Published: (2024)
by: Li, Xi, et al.
Published: (2024)
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation
by: Xu, Heng, et al.
Published: (2024)
by: Xu, Heng, et al.
Published: (2024)
EFU: Enforcing Federated Unlearning via Functional Encryption
by: Mohammadi, Samaneh, et al.
Published: (2025)
by: Mohammadi, Samaneh, et al.
Published: (2025)
Poisoning Attacks and Defenses to Federated Unlearning
by: Wang, Wenbin, et al.
Published: (2025)
by: Wang, Wenbin, et al.
Published: (2025)
FL-PBM: Pre-Training Backdoor Mitigation for Federated Learning
by: Wehbi, Osama, et al.
Published: (2026)
by: Wehbi, Osama, et al.
Published: (2026)
A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning
by: Zhou, Anqi, et al.
Published: (2024)
by: Zhou, Anqi, et al.
Published: (2024)
Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)
by: Ebron, Sheldon C., et al.
Published: (2023)
by: Ebron, Sheldon C., et al.
Published: (2023)
Efficient Language Model Architectures for Differentially Private Federated Learning
by: Ro, Jae Hun, et al.
Published: (2024)
by: Ro, Jae Hun, et al.
Published: (2024)
SecureSplit: Mitigating Backdoor Attacks in Split Learning
by: Dou, Zhihao, et al.
Published: (2026)
by: Dou, Zhihao, et al.
Published: (2026)
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
by: Zhang, Hangtao, et al.
Published: (2023)
by: Zhang, Hangtao, et al.
Published: (2023)
Personalized Federated Learning via Stacking
by: Cantu-Cervini, Emilio
Published: (2024)
by: Cantu-Cervini, Emilio
Published: (2024)
Towards Trustworthy Federated Learning
by: Basharat, Alina, et al.
Published: (2025)
by: Basharat, Alina, et al.
Published: (2025)
DarkFed: A Data-Free Backdoor Attack in Federated Learning
by: Li, Minghui, et al.
Published: (2024)
by: Li, Minghui, et al.
Published: (2024)
DROP: Poison Dilution via Knowledge Distillation for Federated Learning
by: Syros, Georgios, et al.
Published: (2025)
by: Syros, Georgios, et al.
Published: (2025)
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
by: Zuo, Xuhan, et al.
Published: (2024)
by: Zuo, Xuhan, et al.
Published: (2024)
Federated Graph Learning with Adaptive Importance-based Sampling
by: Li, Anran, et al.
Published: (2024)
by: Li, Anran, 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)
by: Zhang, Jianqing, et al.
Published: (2024)
Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning
by: Hosain, Md. Tanzib, et al.
Published: (2025)
by: Hosain, Md. Tanzib, et al.
Published: (2025)
Byzantine-Robust Decentralized Federated Learning
by: Fang, Minghong, et al.
Published: (2024)
by: Fang, Minghong, et al.
Published: (2024)
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning
by: Xing, Zhibo, et al.
Published: (2024)
by: Xing, Zhibo, et al.
Published: (2024)
Differentially Private Online Federated Learning with Correlated Noise
by: Zhang, Jiaojiao, et al.
Published: (2024)
by: Zhang, Jiaojiao, et al.
Published: (2024)
SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)
by: Rieger, Phillip, et al.
Published: (2025)
by: Rieger, Phillip, et al.
Published: (2025)
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning
by: Xu, Zhangchen, et al.
Published: (2024)
by: Xu, Zhangchen, et al.
Published: (2024)
A Survey for Federated Learning Evaluations: Goals and Measures
by: Chai, Di, et al.
Published: (2023)
by: Chai, Di, et al.
Published: (2023)
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning
by: Gong, Zirui, et al.
Published: (2025)
by: Gong, Zirui, et al.
Published: (2025)
Poisoning with A Pill: Circumventing Detection in Federated Learning
by: Guo, Hanxi, et al.
Published: (2024)
by: Guo, Hanxi, et al.
Published: (2024)
DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
by: Mehmood, Haaris, et al.
Published: (2026)
by: Mehmood, Haaris, et al.
Published: (2026)
FedBaF: Federated Learning Aggregation Biased by a Foundation Model
by: Park, Jong-Ik, et al.
Published: (2024)
by: Park, Jong-Ik, et al.
Published: (2024)
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
by: Xu, Yichang, et al.
Published: (2024)
by: Xu, Yichang, et al.
Published: (2024)
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
by: Tan, Qi, et al.
Published: (2024)
by: Tan, Qi, et al.
Published: (2024)
Differentially Private Clustered Federated Learning
by: Malekmohammadi, Saber, et al.
Published: (2024)
by: Malekmohammadi, Saber, et al.
Published: (2024)
Anomalous Client Detection in Federated Learning
by: Thakur, Dipanwita, et al.
Published: (2024)
by: Thakur, Dipanwita, et al.
Published: (2024)
A Survey on Decentralized Federated Learning
by: Gabrielli, Edoardo, et al.
Published: (2023)
by: Gabrielli, Edoardo, et al.
Published: (2023)
Provably Robust Federated Reinforcement Learning
by: Fang, Minghong, et al.
Published: (2025)
by: Fang, Minghong, et al.
Published: (2025)
SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable Masks
by: Yan, Peishen, et al.
Published: (2023)
by: Yan, Peishen, et al.
Published: (2023)
Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
by: Zhang, Baolei, et al.
Published: (2025)
by: Zhang, Baolei, et al.
Published: (2025)
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation
by: Xia, Tong, et al.
Published: (2023)
by: Xia, Tong, et al.
Published: (2023)
Similar Items
-
Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
by: Xu, Jiahao, et al.
Published: (2025) -
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation
by: Xu, Jiahao, et al.
Published: (2024) -
Upcycling Noise for Federated Unlearning
by: Chen, Jianan, et al.
Published: (2024) -
Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
by: Li, Xi, et al.
Published: (2024) -
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation
by: Xu, Heng, et al.
Published: (2024)