Hugging Face Trending Papers July 8, 2026

FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation

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Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself. Recently, centralized Taking Away Training Data (TATD) attacks have shown that malicious training could abuse the memorization capacity of deep models to store and later recover training data.

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arXiv AI

Jul 9

FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation

arXiv:2607. 07314v1 Announce Type: cross Abstract: Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself.

By Chongkai Li, Bang Zhang, Wenjian Luo

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arXiv Machine Learning

Jul 7

GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning

arXiv:2412. 12640v2 Announce Type: replace Abstract: The increasing demand for data privacy, alongside the benefits of aggregating data from networked devices, has catalyzed the emergence of federated learning (FL).

By Rui Zhang, Ka-Ho Chow

llms safety

arXiv Machine Learning

Jun 2

Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning

arXiv:2606. 00986v1 Announce Type: new Abstract: Federated learning (FL) enables multiple data holders to train machine learning models collaboratively without centralizing raw data, making it useful in privacy sensitive domains such as healthcare and institutional data sharing.

By Ivo Osterberg Nilsson, Maximilian Birr Engvall, Viktor Valadi, Teddy Lazebnik

llms benchmarks safety

arXiv Machine Learning

Jun 16

Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning

arXiv:2606. 17035v1 Announce Type: new Abstract: Prior research suggests that differential privacy (DP) inherently enhances the robustness of federated learning (FL) against backdoor attacks.

By Xiaolin Li, Ning Wang, Ninghui Li, Wenhai Sun

benchmarks safety

arXiv Machine Learning

Jun 17

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

arXiv:2606. 17110v1 Announce Type: cross Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user conversations containing secrets.

By Md Abdullah Al Mamun, Ngoc Phu Doan, Pedram Zaree, Ihsen Alouani, Nael Abu-Ghazaleh

llms multimodal safety

Hugging Face Trending Papers

Jun 1

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.

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Related stories

FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation

GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning

Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning

Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning