Hugging Face Trending Papers June 1, 2026

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

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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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arXiv Machine Learning

Jun 2

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

arXiv:2606. 02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity.

By Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella Sun

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arXiv Machine Learning

Jun 18

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

arXiv:2502. 17748v4 Announce Type: replace Abstract: Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks.

By Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki

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arXiv Machine Learning

1d ago

An Adaptive Gradient Clipping and Noise Injection Mechanism for Differentially Private Federated Learning

arXiv:2608. 15153v1 Announce Type: cross Abstract: Differentially private federated learning must balance privacy protection against model accuracy and training efficiency.

By Wenjing Wei, Alla Jammine, Farid Nait-Abdesselam

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arXiv Machine Learning

Jun 16

DP-Hype: Federated Differentially Private Hyperparameter Search

arXiv:2510. 04902v3 Announce Type: replace Abstract: Tuning hyperparameters in federated machine learning can substantially impact model performance.

By Johannes Liebenow, Thorsten Peinemann, Esfandiar Mohammadi

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arXiv Machine Learning

Jun 4

DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data

arXiv:2606. 04399v1 Announce Type: new Abstract: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server.

By Yunsheng Yuan, Xue Xiao, Lina Wang, Feng Li

agents benchmarks safety

arXiv Machine Learning

Jun 9

Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation

arXiv:2604. 07125v2 Announce Type: replace-cross Abstract: This article presents DDP-SA, a scalable privacy-preserving federated learning framework that jointly leverages client-side local differential privacy (LDP) and full-threshold additive secret sharing (ASS) for secure aggregation.

By Wenjing Wei, Farid Nait-Abdesselam, Alla Jammine

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IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

An Adaptive Gradient Clipping and Noise Injection Mechanism for Differentially Private Federated Learning

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Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation