Cover Image

Data Overvaluation Attack and Truthful Data Valuation in Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zheng, Shuyuan, Cai, Sudong, Xiao, Chuan, Cao, Yang, Qin, Jianbin, Yoshikawa, Masatoshi, Onizuka, Makoto
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

Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)
by: Zheng, Shuyuan, et al.
Published: (2022)

Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems
by: Ko, Ronny, et al.
Published: (2025)

Data Valuation and Detections in Federated Learning
by: Li, Wenqian, et al.
Published: (2023)

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

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

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

KG-DF: A Black-box Defense Framework against Jailbreak Attacks Based on Knowledge Graphs
by: Liu, Shuyuan, et al.
Published: (2025)

Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks
by: Zheng, Lele, et al.
Published: (2024)

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

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

Challenges in Enabling Private Data Valuation
by: Fu, Yiwei, et al.
Published: (2026)

Privacy-Enhanced Database Synthesis for Benchmark Publishing (Technical Report)
by: Ge, Yunqing, et al.
Published: (2024)

Persistent Backdoor Attacks under Continual Fine-Tuning of LLMs
by: Cui, Jing, et al.
Published: (2025)

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

Turning Generative Models Degenerate: The Power of Data Poisoning Attacks
by: Jiang, Shuli, et al.
Published: (2024)

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

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

Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering
by: Lyu, Xingyu, et al.
Published: (2025)

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

Privacy Attack in Federated Learning is Not Easy: An Experimental Study
by: Zhu, Hangyu, et al.
Published: (2024)

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

Recent Advances in Attack and Defense Approaches of Large Language Models
by: Cui, Jing, et al.
Published: (2024)

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

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

Secure Federated Data Distillation
by: Arazzi, Marco, et al.
Published: (2025)

Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing
by: Eghtesad, Taha, et al.
Published: (2026)

SAB:A Stealing and Robust Backdoor Attack based on Steganographic Algorithm against Federated Learning
by: Xu, Weida, et al.
Published: (2024)

Approximate and Weighted Data Reconstruction Attack in Federated Learning
by: Song, Yongcun, et al.
Published: (2023)

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

False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning
by: Uddin, Md Raihan, et al.
Published: (2024)

Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI
by: Ye, Dayong, et al.
Published: (2025)

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

Repurposing Backdoors for Good: Ephemeral Intrinsic Proofs for Verifiable Aggregation in Cross-silo Federated Learning
by: Qin, Xian, et al.
Published: (2026)

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

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

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

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

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

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

Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective
by: Wang, Zifan, et al.
Published: (2024)