Image de couverture de livre

Local Model Reconstruction Attacks in Federated Learning and their Uses

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Driouich, Ilias, Xu, Chuan, Neglia, Giovanni, Giroire, Frederic, Thomas, Eoin
Format:	Preprint
Publié:	2022
Sujets:	Machine Learning Artificial Intelligence Cryptography and Security
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Attribute Inference Attacks for Federated Regression Tasks
par: Diana, Francesco, et autres
Publié: (2024)

Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
par: Diana, Francesco, et autres
Publié: (2025)

No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning
par: Diana, Francesco, et autres
Publié: (2026)

UIFV: Data Reconstruction Attack in Vertical Federated Learning
par: Yang, Jirui, et autres
Publié: (2024)

Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
par: Zheng, Shuyuan, et autres
Publié: (2025)

ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
par: Xu, Zhangchen, et autres
Publié: (2024)

SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
par: Singampalli, Akhil, et autres
Publié: (2024)

Approximate and Weighted Data Reconstruction Attack in Federated Learning
par: Song, Yongcun, et autres
Publié: (2023)

Practical Feasibility of Gradient Inversion Attacks in Federated Learning
par: Valadi, Viktor, et autres
Publié: (2025)

Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
par: Zuo, Shiyuan, et autres
Publié: (2024)

Structure-Aware Distributed Backdoor Attacks in Federated Learning
par: Jian, Wang, et autres
Publié: (2026)

EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
par: Meerza, Syed Irfan Ali, et autres
Publié: (2024)

Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning
par: Foroughi, Mohammad Hadi, et autres
Publié: (2026)

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
par: Zhang, Heyi, et autres
Publié: (2025)

Backdoor Attack on Vertical Federated Graph Neural Network Learning
par: Yang, Jirui, et autres
Publié: (2024)

Client-Side Patching against Backdoor Attacks in Federated Learning
par: Molina-Coronado, Borja
Publié: (2024)

Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning
par: Shan, Junjie, et autres
Publié: (2024)

Reconstructing Training Data from Adapter-based Federated Large Language Models
par: Chen, Silong, et autres
Publié: (2026)

Tackling the Local Bias in Federated Graph Learning
par: Zhang, Binchi, et autres
Publié: (2021)

Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients
par: Yuan, Jinsheng, et autres
Publié: (2025)

FLARE: A Wireless Side-Channel Fingerprinting Attack on Federated Learning
par: Shuvo, Md Nahid Hasan, et autres
Publié: (2025)

Enhancing IoT Security Against DDoS Attacks through Federated Learning
par: Shirvani, Ghazaleh, et autres
Publié: (2024)

Res-MIA: A Training-Free Resolution-Based Membership Inference Attack on Federated Learning Models
par: Zare, Mohammad, et autres
Publié: (2026)

Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection
par: Gabrielli, Edoardo, et autres
Publié: (2023)

Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
par: Obioma, Chibueze Peace, et autres
Publié: (2025)

Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning
par: Ahamed, Sayyed Farid, et autres
Publié: (2024)

Optimal Defenses Against Gradient Reconstruction Attacks
par: Chen, Yuxiao, et autres
Publié: (2024)

False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning
par: Uddin, Md Raihan, et autres
Publié: (2024)

Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients
par: Wu, Leming, et autres
Publié: (2025)

Shape and Substance: Dual-Layer Side-Channel Attacks on Local Vision-Language Models
par: Hadad, Eyal, et autres
Publié: (2026)

GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV
par: Talpur, Anum, et autres
Publié: (2022)

Evaluating Privacy Leakage in Split Learning
par: Qiu, Xinchi, et autres
Publié: (2023)

Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems
par: Jean, Abile, et autres
Publié: (2026)

Federated Learning in Practice: Reflections and Projections
par: Daly, Katharine, et autres
Publié: (2024)

Universal Black-Box Reward Poisoning Attack against Offline Reinforcement Learning
par: Xu, Yinglun, et autres
Publié: (2024)

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
par: Li, Yuecheng, et autres
Publié: (2024)

Hijack Vertical Federated Learning Models As One Party
par: Qiu, Pengyu, et autres
Publié: (2022)

Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
par: Xu, Yukai, et autres
Publié: (2024)

Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
par: Yu, Sixing, et autres
Publié: (2023)

Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning
par: Shuvo, Md Nahid Hasan, et autres
Publié: (2025)