:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Ma, Zhixuan, Gao, Haichang, Huang, Junxiang, Wang, Ping
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Cryptography and Security
Acceso en línea:	https://arxiv.org/abs/2504.21436
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation
por: Shi, Haonan, et al.
Publicado: (2025)

Federated Learning Clients Clustering with Adaptation to Data Drifts
por: Li, Minghao, et al.
Publicado: (2024)

Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates
por: Wang, Chuanyin, et al.
Publicado: (2025)

LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation
por: He, Ying, et al.
Publicado: (2024)

Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
por: Xu, Jiahao, et al.
Publicado: (2025)

SLVR: Securely Leveraging Client Validation for Robust Federated Learning
por: Choi, Jihye, et al.
Publicado: (2025)

HumorReject: Decoupling LLM Safety from Refusal Prefix via A Little Humor
por: Wu, Zihui, et al.
Publicado: (2025)

The More is not the Merrier: Investigating the Effect of Client Size on Federated Learning
por: Wallach, Eleanor, et al.
Publicado: (2025)

Building Gradient Bridges: Label Leakage from Restricted Gradient Sharing in Federated Learning
por: Zhang, Rui, et al.
Publicado: (2024)

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
por: Allouah, Youssef, et al.
Publicado: (2024)

Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
por: Mozaffari, Hamid, et al.
Publicado: (2024)

Sybil-based Virtual Data Poisoning Attacks in Federated Learning
por: Zhu, Changxun, et al.
Publicado: (2025)

Federated Learning with Only Positive Labels by Exploring Label Correlations
por: An, Xuming, et al.
Publicado: (2024)

How to Forget Clients in Federated Online Learning to Rank?
por: Wang, Shuyi, et al.
Publicado: (2024)

Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
por: Xu, Yichang, et al.
Publicado: (2024)

History-Aware and Dynamic Client Contribution in Federated Learning
por: Ghosh, Bishwamittra, et al.
Publicado: (2024)

Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients
por: Wu, Leming, et al.
Publicado: (2025)

EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
por: Cheng, Tong, et al.
Publicado: (2025)

Federated Learning With Individualized Privacy Through Client Sampling
por: Lange, Lucas, et al.
Publicado: (2025)

Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks
por: Díaz, Judith Sáinz-Pardo, et al.
Publicado: (2025)

AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator
por: Hossain, Md. Kamrul, et al.
Publicado: (2025)

Decaf: Data Distribution Decompose Attack against Federated Learning
por: Dai, Zhiyang, et al.
Publicado: (2024)

Random Client Selection on Contrastive Federated Learning for Tabular Data
por: Ginanjar, Achmad, et al.
Publicado: (2025)

Recovering Labels from Local Updates in Federated Learning
por: Chen, Huancheng, et al.
Publicado: (2024)

Toward Malicious Clients Detection in Federated Learning
por: Dou, Zhihao, et al.
Publicado: (2025)

Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
por: Yao, Duanyi, et al.
Publicado: (2024)

FLClear: Visually Verifiable Multi-Client Watermarking for Federated Learning
por: Gu, Chen, et al.
Publicado: (2025)

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

Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration
por: Zhang, Yiwei, et al.
Publicado: (2025)

Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning
por: Lyu, Xiaoting, et al.
Publicado: (2024)

Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue
por: Brunner, David, et al.
Publicado: (2024)

Anomalous Client Detection in Federated Learning
por: Thakur, Dipanwita, et al.
Publicado: (2024)

Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing
por: Takagi, Shun, et al.
Publicado: (2025)

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients
por: Zhang, Jianyi, et al.
Publicado: (2025)

Whispers in the Machine: Confidentiality in Agentic Systems
por: Evertz, Jonathan, et al.
Publicado: (2024)

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

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

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

FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning
por: Alsenani, Yousef, et al.
Publicado: (2024)

BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning
por: Jiang, Zhengyuan, et al.
Publicado: (2024)