Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
Fuente:
arXiv
Guardado en:
| Autores principales: | Xu, Jiahao, Zhang, Zikai, Hu, Rui |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Identify Backdoored Model in Federated Learning via Individual Unlearning
por: Xu, Jiahao, et al.
Publicado: (2024)
por: Xu, Jiahao, et al.
Publicado: (2024)
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation
por: Xu, Jiahao, et al.
Publicado: (2024)
por: Xu, Jiahao, et al.
Publicado: (2024)
A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning
por: Zhou, Anqi, et al.
Publicado: (2024)
por: Zhou, Anqi, et al.
Publicado: (2024)
SecureSplit: Mitigating Backdoor Attacks in Split Learning
por: Dou, Zhihao, et al.
Publicado: (2026)
por: Dou, Zhihao, et al.
Publicado: (2026)
Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
por: Li, Xi, et al.
Publicado: (2024)
por: Li, Xi, et al.
Publicado: (2024)
FL-PBM: Pre-Training Backdoor Mitigation for Federated Learning
por: Wehbi, Osama, et al.
Publicado: (2026)
por: Wehbi, Osama, et al.
Publicado: (2026)
DarkFed: A Data-Free Backdoor Attack in Federated Learning
por: Li, Minghui, et al.
Publicado: (2024)
por: Li, Minghui, et al.
Publicado: (2024)
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
por: Zhang, Hangtao, et al.
Publicado: (2023)
por: Zhang, Hangtao, et al.
Publicado: (2023)
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
por: Tan, Qi, et al.
Publicado: (2024)
por: Tan, Qi, et al.
Publicado: (2024)
SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)
por: Rieger, Phillip, et al.
Publicado: (2025)
por: Rieger, Phillip, et al.
Publicado: (2025)
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
por: Xu, Yichang, et al.
Publicado: (2024)
por: Xu, Yichang, et al.
Publicado: (2024)
SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable Masks
por: Yan, Peishen, et al.
Publicado: (2023)
por: Yan, Peishen, et al.
Publicado: (2023)
Poisoning Attacks and Defenses to Federated Unlearning
por: Wang, Wenbin, et al.
Publicado: (2025)
por: Wang, Wenbin, et al.
Publicado: (2025)
Beyond Denial-of-Service: The Puppeteer's Attack for Fine-Grained Control in Ranking-Based Federated Learning
por: Chen, Zhihao, et al.
Publicado: (2026)
por: Chen, Zhihao, et al.
Publicado: (2026)
Poisoning with A Pill: Circumventing Detection in Federated Learning
por: Guo, Hanxi, et al.
Publicado: (2024)
por: Guo, Hanxi, et al.
Publicado: (2024)
Anomalous Client Detection in Federated Learning
por: Thakur, Dipanwita, et al.
Publicado: (2024)
por: Thakur, Dipanwita, et al.
Publicado: (2024)
Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning
por: Mo, Wenjin, et al.
Publicado: (2025)
por: Mo, Wenjin, et al.
Publicado: (2025)
Toward Malicious Clients Detection in Federated Learning
por: Dou, Zhihao, et al.
Publicado: (2025)
por: Dou, Zhihao, et al.
Publicado: (2025)
Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)
por: Ebron, Sheldon C., et al.
Publicado: (2023)
por: Ebron, Sheldon C., et al.
Publicado: (2023)
Personalized Federated Learning via Stacking
por: Cantu-Cervini, Emilio
Publicado: (2024)
por: Cantu-Cervini, Emilio
Publicado: (2024)
The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks
por: Nguyen, Manh V., et al.
Publicado: (2025)
por: Nguyen, Manh V., et al.
Publicado: (2025)
Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning
por: Xhemrishi, Marvin, et al.
Publicado: (2025)
por: Xhemrishi, Marvin, et al.
Publicado: (2025)
Towards Trustworthy Federated Learning
por: Basharat, Alina, et al.
Publicado: (2025)
por: Basharat, Alina, et al.
Publicado: (2025)
Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning
por: Wang, Fei, et al.
Publicado: (2025)
por: Wang, Fei, et al.
Publicado: (2025)
Upcycling Noise for Federated Unlearning
por: Chen, Jianan, et al.
Publicado: (2024)
por: Chen, Jianan, et al.
Publicado: (2024)
Efficient Language Model Architectures for Differentially Private Federated Learning
por: Ro, Jae Hun, et al.
Publicado: (2024)
por: Ro, Jae Hun, et al.
Publicado: (2024)
A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT
por: Mahmoud, Taha M., et al.
Publicado: (2025)
por: Mahmoud, Taha M., et al.
Publicado: (2025)
DROP: Poison Dilution via Knowledge Distillation for Federated Learning
por: Syros, Georgios, et al.
Publicado: (2025)
por: Syros, Georgios, et al.
Publicado: (2025)
Federated Graph Learning with Adaptive Importance-based Sampling
por: Li, Anran, et al.
Publicado: (2024)
por: Li, Anran, et al.
Publicado: (2024)
Byzantine-Robust Decentralized Federated Learning
por: Fang, Minghong, et al.
Publicado: (2024)
por: Fang, Minghong, et al.
Publicado: (2024)
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning
por: Xing, Zhibo, et al.
Publicado: (2024)
por: Xing, Zhibo, et al.
Publicado: (2024)
TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems
por: Di Gennaro, Marco, et al.
Publicado: (2025)
por: Di Gennaro, Marco, et al.
Publicado: (2025)
Federated Learning for Pediatric Pneumonia Detection: Enabling Collaborative Diagnosis Without Sharing Patient Data
por: Jimenez-Gutierrez, Daniel M., et al.
Publicado: (2025)
por: Jimenez-Gutierrez, Daniel M., et al.
Publicado: (2025)
Differentially Private Online Federated Learning with Correlated Noise
por: Zhang, Jiaojiao, et al.
Publicado: (2024)
por: Zhang, Jiaojiao, et al.
Publicado: (2024)
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning
por: Xu, Zhangchen, et al.
Publicado: (2024)
por: Xu, Zhangchen, et al.
Publicado: (2024)
A Survey for Federated Learning Evaluations: Goals and Measures
por: Chai, Di, et al.
Publicado: (2023)
por: Chai, Di, et al.
Publicado: (2023)
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning
por: Gong, Zirui, et al.
Publicado: (2025)
por: Gong, Zirui, et al.
Publicado: (2025)
DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
por: Mehmood, Haaris, et al.
Publicado: (2026)
por: Mehmood, Haaris, et al.
Publicado: (2026)
PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks
por: Zhang, Hongliang, et al.
Publicado: (2025)
por: Zhang, Hongliang, et al.
Publicado: (2025)
Mitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theory
por: Wehbi, Osama, et al.
Publicado: (2026)
por: Wehbi, Osama, et al.
Publicado: (2026)
Ejemplares similares
-
Identify Backdoored Model in Federated Learning via Individual Unlearning
por: Xu, Jiahao, et al.
Publicado: (2024) -
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation
por: Xu, Jiahao, et al.
Publicado: (2024) -
A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning
por: Zhou, Anqi, et al.
Publicado: (2024) -
SecureSplit: Mitigating Backdoor Attacks in Split Learning
por: Dou, Zhihao, et al.
Publicado: (2026) -
Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
por: Li, Xi, et al.
Publicado: (2024)