Imagen de Portada

Privacy Attack in Federated Learning is Not Easy: An Experimental Study

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhu, Hangyu, Huang, Liyuan, Xie, Zhenping
Formato:	Preprint
Publicado:	2024
Materias:	Cryptography and Security Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Towards Family-Grouped Hierarchical Federated Learning on Sub-5KB Models: A Feasibility Study of Privacy-Preserving ECG Monitoring for Ultra-Resource-Constrained Wearables
por: Wu, Hangyu
Publicado: (2026)

Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
por: Xie, Chulin, et al.
Publicado: (2022)

Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response
por: Ozturk, Ozer, et al.
Publicado: (2025)

Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
por: Xu, Runhua, et al.
Publicado: (2025)

Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
por: Zhang, Xiaojin, et al.
Publicado: (2025)

FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
por: Han, Shanshan, et al.
Publicado: (2023)

Model Inversion Attack against Federated Unlearning
por: Zhou, Lei, et al.
Publicado: (2025)

Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy
por: Piran, Fardin Jalil, et al.
Publicado: (2025)

FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
por: Gu, Hanlin, et al.
Publicado: (2024)

Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning
por: Ahamed, Sayyed Farid, et al.
Publicado: (2024)

Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
por: Díaz, Judith Sáinz-Pardo, et al.
Publicado: (2026)

Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks
por: Guo, Pengxin, et al.
Publicado: (2025)

OASIS: Offsetting Active Reconstruction Attacks in Federated Learning
por: Jeter, Tre' R., et al.
Publicado: (2023)

Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare
por: Jeong, Hyejun, et al.
Publicado: (2024)

TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
por: Xu, Runhua, et al.
Publicado: (2025)

Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
por: Diana, Francesco, et al.
Publicado: (2025)

Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks
por: Alharbi, Ebtisaam, et al.
Publicado: (2025)

PPBFL: A Privacy Protected Blockchain-based Federated Learning Model
por: Li, Yang, et al.
Publicado: (2024)

How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers
por: Zhang, Guangsheng, et al.
Publicado: (2022)

Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
por: Kumar, K Naveen, et al.
Publicado: (2024)

Federated Learning under Attack: Improving Gradient Inversion for Batch of Images
por: Leite, Luiz, et al.
Publicado: (2024)

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
por: Carney, Jane, et al.
Publicado: (2025)

GUIDE: Enhancing Gradient Inversion Attacks in Federated Learning with Denoising Models
por: Carletti, Vincenzo, et al.
Publicado: (2025)

FedCC: Robust Federated Learning against Model Poisoning Attacks
por: Jeong, Hyejun, et al.
Publicado: (2022)

Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review
por: ElZemity, Adel, et al.
Publicado: (2024)

BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse
por: Liu, Wenbo, et al.
Publicado: (2024)

Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications
por: Taiello, Riccardo, et al.
Publicado: (2024)

A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data
por: Negoya, Yusaku, et al.
Publicado: (2025)

Zero-Knowledge Federated Learning: A New Trustworthy and Privacy-Preserving Distributed Learning Paradigm
por: Wang, Taotao, et al.
Publicado: (2025)

VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Masking
por: Tan, Juntao, et al.
Publicado: (2025)

Concept-Aware Privacy Mechanisms for Defending Embedding Inversion Attacks
por: Tsai, Yu-Che, et al.
Publicado: (2026)

RAG Security and Privacy: Formalizing the Threat Model and Attack Surface
por: Arzanipour, Atousa, et al.
Publicado: (2025)

Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
por: Dong, Wenhan, et al.
Publicado: (2024)

Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning
por: Li, Jiaxin, et al.
Publicado: (2024)

VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learning
por: Tan, Juntao, et al.
Publicado: (2025)

MedHE: Communication-Efficient Privacy-Preserving Federated Learning with Adaptive Gradient Sparsification for Healthcare
por: Yesmin, Farjana
Publicado: (2025)

VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption
por: Cai, Nina, et al.
Publicado: (2025)

FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models
por: Ye, Pukang, et al.
Publicado: (2025)

FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users
por: Rahulamathavan, Yogachandran, et al.
Publicado: (2023)

FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning
por: Zhang, Zhuo, et al.
Publicado: (2024)