arXiv Machine Learning June 18, 2026 By Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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.

safety

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

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 AI

Jul 20

Poison to Detect: Detection of Targeted Overfitting in Federated Learning

arXiv:2509. 11974v2 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training among clients without centralising data, making it a widely adopted privacy-enhancing technology (PET).

By Soumia Zohra El Mestari, Maciej Krzysztof Zuziak, Gabriele Lenzini

safety

arXiv Machine Learning

Jul 21

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering

arXiv:2310. 16152v5 Announce Type: replace-cross Abstract: Federated learning (FL) has become a key component in various language modeling applications such as machine translation, next-word prediction, and medical record analysis.

By Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Kang Gu, Najrin Sultana, Shagufta Mehnaz

llms nlp safety

arXiv AI

Jun 10

From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning

arXiv:2606. 10595v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning.

By Huong Nguyen, Micka\"el Bettinelli, Amirhossein Ghaffari, Alexandre Benoit, Hong-Tri Nguyen, Susanna Pirttikangas, Lauri Lov\'en

safety

Related stories

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

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

Poison to Detect: Detection of Targeted Overfitting in Federated Learning

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering

From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning