Cover Image

Practical Feasibility of Gradient Inversion Attacks in Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Valadi, Viktor, Åkesson, Mattias, Östman, Johan, Hoseini, Fazeleh, Toor, Salman, Hellander, Andreas
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Poisoning Attacks on Federated Learning for Autonomous Driving
by: Garg, Sonakshi, et al.
Published: (2024)

Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning
by: Shan, Junjie, et al.
Published: (2024)

No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning
by: Diana, Francesco, et al.
Published: (2026)

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks
by: Papadopoulos, Georgios, et al.
Published: (2025)

GI-PIP: Do We Require Impractical Auxiliary Dataset for Gradient Inversion Attacks?
by: Sun, Yu, et al.
Published: (2024)

Practical Bayes-Optimal Membership Inference Attacks
by: Lassila, Marcus, et al.
Published: (2025)

Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy
by: Shi, Yichuan, et al.
Published: (2024)

ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
by: Gong, Zirui, et al.
Published: (2026)

Unlearning Inversion Attacks for Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2025)

Federated Learning in Practice: Reflections and Projections
by: Daly, Katharine, et al.
Published: (2024)

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

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

Combining Stochastic Defenses to Resist Gradient Inversion: An Ablation Study
by: Scheliga, Daniel, et al.
Published: (2022)

Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
by: Zuo, Shiyuan, et al.
Published: (2024)

UIFV: Data Reconstruction Attack in Vertical Federated Learning
by: Yang, Jirui, et al.
Published: (2024)

Local Model Reconstruction Attacks in Federated Learning and their Uses
by: Driouich, Ilias, et al.
Published: (2022)

Structure-Aware Distributed Backdoor Attacks in Federated Learning
by: Jian, Wang, et al.
Published: (2026)

Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning
by: Foroughi, Mohammad Hadi, et al.
Published: (2026)

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
by: Zhang, Heyi, et al.
Published: (2025)

Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
by: Zheng, Shuyuan, et al.
Published: (2025)

Backdoor Attack on Vertical Federated Graph Neural Network Learning
by: Yang, Jirui, et al.
Published: (2024)

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

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

A New Federated Learning Framework Against Gradient Inversion Attacks
by: Guo, Pengxin, et al.
Published: (2024)

Optimal Defenses Against Gradient Reconstruction Attacks
by: Chen, Yuxiao, et al.
Published: (2024)

Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
by: Wu, Zhaomin, et al.
Published: (2024)

Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients
by: Yuan, Jinsheng, et al.
Published: (2025)

FLARE: A Wireless Side-Channel Fingerprinting Attack on Federated Learning
by: Shuvo, Md Nahid Hasan, et al.
Published: (2025)

Enhancing IoT Security Against DDoS Attacks through Federated Learning
by: Shirvani, Ghazaleh, et al.
Published: (2024)

Model Inversion Attacks on Llama 3: Extracting PII from Large Language Models
by: Sivashanmugam, Sathesh P.
Published: (2025)

zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning
by: Wang, Zhipeng, et al.
Published: (2023)

Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
by: Obioma, Chibueze Peace, et al.
Published: (2025)

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

ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
by: Xu, Zhangchen, et al.
Published: (2024)

Attribute Inference Attacks for Federated Regression Tasks
by: Diana, Francesco, et al.
Published: (2024)

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

SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
by: Singampalli, Akhil, et al.
Published: (2024)

EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
by: Meerza, Syed Irfan Ali, et al.
Published: (2024)

Jailbreaking LLMs Without Gradients or Priors: Effective and Transferable Attacks
by: Nurlanov, Zhakshylyk, et al.
Published: (2026)

Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning
by: Li, Jianwei, et al.
Published: (2023)