Hugging Face Trending Papers June 3, 2026

Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving

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Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis. Federated learning (FL) has attracted growing attention as a promising framework for collaborative model development, as it allows multiple institutions to jointly train predictive models without directly sharing or centralizing raw data.

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arXiv Machine Learning

Jun 4

Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving

arXiv:2606. 04338v1 Announce Type: new Abstract: Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis.

By Xixi Tian, Di Wu, Xiang Liu, Yiziting Zhu, Yujie Li, Xin Shu, Bin Yi

safety

arXiv Machine Learning

Jul 10

Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction

arXiv:2607. 08595v1 Announce Type: new Abstract: Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets.

By Hyunho Mo, Djura Smits, Mahlet A. Birhanu, Maarten J. G. Leening, Daniel Bos, Pim van der Harst, Esther E. Bron

safety

arXiv AI

Jul 23

Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction

arXiv:2607. 19532v1 Announce Type: cross Abstract: Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalised cancer care.

By Ruth Amey, Muhammad Arifur Rahman, Taha Osman, Nicholas Shopland, Andy Burton, Mufti Mahmud, David J. Brown

multimodal safety

arXiv Machine Learning

Jul 27

A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx

arXiv:2508. 10017v2 Announce Type: replace-cross Abstract: Federated Learning (FL) presents a groundbreaking approach for collaborative health research, allowing model training on decentralized data while safeguarding patient privacy.

By Rodrigo Tertulino

safety

Hugging Face Trending Papers

Sep 2

Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems

The paper introduces DP‑BR‑FedAvg, a federated learning framework that combines Gaussian‑mechanism differential privacy with a coordinate‑wise trimmed‑mean Byzantine‑robust aggregation rule. It is evaluated on a simulated cross‑institutional classification task for fraud and clinical‑risk scoring, showing that plain FedAvg fails when a quarter of twenty clients are Byzantine, while DP‑BR‑FedAvg recovers more signal and bounds privacy loss. The study demonstrates that privacy and robustness interact, and system design for regulated, adversarial, cross‑institutional settings must account for this interaction.

safety

arXiv Machine Learning

Jun 4

FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data

arXiv:2605. 18936v2 Announce Type: replace Abstract: Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors.

By Nuredin Ali Abdelkadir, Anjali Ratnam, Zeerak Talat, Stevie Chancellor

benchmarks safety

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Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving

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Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction

A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx

Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems

FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data