:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chen, Jian, Lin, Zehui, Lin, Wanyu, Shi, Wenlong, Yin, Xiaoyan, Wang, Di
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security
Online Access:	https://arxiv.org/abs/2501.11848
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Label Inference Attacks against Federated Unlearning
by: Wang, Wei, et al.
Published: (2025)

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
by: Kavuri, Likhitha Annapurna, et al.
Published: (2025)

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

FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
by: Xhemrishi, Marvin, et al.
Published: (2023)

FedMUP: Federated Learning driven Malicious User Prediction Model for Secure Data Distribution in Cloud Environments
by: Gupta, Kishu, et al.
Published: (2024)

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

FedSGT: Exact Federated Unlearning via Sequential Group-based Training
by: Zhang, Bokang, et al.
Published: (2025)

Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning
by: Hu, Hongsheng, et al.
Published: (2024)

Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
by: Bertran, Martin, et al.
Published: (2024)

FedRecAttack: Model Poisoning Attack to Federated Recommendation
by: Rong, Dazhong, et al.
Published: (2022)

Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning
by: Jia, Yuqi, et al.
Published: (2024)

Privacy-Preserving Federated Learning Scheme with Mitigating Model Poisoning Attacks: Vulnerabilities and Countermeasures
by: Wu, Jiahui, et al.
Published: (2025)

The Vulnerability of LLM Rankers to Prompt Injection Attacks
by: Yin, Yu, et al.
Published: (2026)

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

FedGuard: A Diverse-Byzantine-Robust Mechanism for Federated Learning with Major Malicious Clients
by: Jiang, Haocheng, et al.
Published: (2025)

Attack Effect Model based Malicious Behavior Detection
by: Wang, Limin, et al.
Published: (2025)

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

FedFDP: Fairness-Aware Federated Learning with Differential Privacy
by: Ling, Xinpeng, et al.
Published: (2024)

Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2026)

FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation
by: Wang, Zhichao, et al.
Published: (2024)

Blockchain-enabled Trustworthy Federated Unlearning
by: Lin, Yijing, et al.
Published: (2024)

Malla: Demystifying Real-world Large Language Model Integrated Malicious Services
by: Lin, Zilong, et al.
Published: (2024)

MalModel: Hiding Malicious Payload in Mobile Deep Learning Models with Black-box Backdoor Attack
by: Hua, Jiayi, et al.
Published: (2024)

MalTool: Malicious Tool Attacks on LLM Agents
by: Hu, Yuepeng, et al.
Published: (2026)

DarkFed: A Data-Free Backdoor Attack in Federated Learning
by: Li, Minghui, et al.
Published: (2024)

SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
by: Mulay, Ajinkya Kiran, et al.
Published: (2024)

FedGMark: Certifiably Robust Watermarking for Federated Graph Learning
by: Yang, Yuxin, et al.
Published: (2024)

Models Are Codes: Towards Measuring Malicious Code Poisoning Attacks on Pre-trained Model Hubs
by: Zhao, Jian, et al.
Published: (2024)

Poisoning Attacks and Defenses to Federated Unlearning
by: Wang, Wenbin, et al.
Published: (2025)

Robust Federated Learning for Malicious Clients using Loss Trend Deviation Detection
by: Bhaskar, Deepthy K, et al.
Published: (2026)

Toward More Generalized Malicious URL Detection Models
by: Tsai, YunDa, et al.
Published: (2022)

Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend
by: Liu, Yige, et al.
Published: (2026)

The Pulse of Fileless Cryptojacking Attacks: Malicious PowerShell Scripts
by: Varlioglu, Said, et al.
Published: (2024)

Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain
by: Liu, Hanzhi, et al.
Published: (2026)

AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens
by: Lu, Lin, et al.
Published: (2024)

SINCon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor Detection
by: Zhang, Mingqing, et al.
Published: (2025)

Malicious Agent Detection for Robust Multi-Agent Collaborative Perception
by: Zhao, Yangheng, et al.
Published: (2023)

Conflicts Make Large Reasoning Models Vulnerable to Attacks
by: Liu, Honghao, et al.
Published: (2026)

Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy
by: Xu, Shuangqing, et al.
Published: (2024)

An Analysis of Malicious Packages in Open-Source Software in the Wild
by: Zhou, Xiaoyan, et al.
Published: (2024)