arXiv AI July 23, 2026 By Rodrigo Tertulino, Laercio Alencar, Ricardo Almeida

Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets

arXiv:2607. 19403v1 Announce Type: cross Abstract: Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic environments and deployment in real multicenter healthcare settings.

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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

arXiv AI

Jul 23

SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework

arXiv:2607. 19524v1 Announce Type: cross Abstract: Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by restricted data sharing, client heterogeneity, class imbalance, and the lack of realistic tabular electronic health record (EHR) benchmarks.

By Akarsh K Nair, Muhammad Arifur Rahman, Nicholas Shopland, Andy Burton, Jun He, Yuan Shen, David Baldwin, Emma O'Dowd, Amna Burzic, Mufti Mahmud, David J. Brown

diffusion benchmarks safety

arXiv Machine Learning

Jul 27

A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data

arXiv:2509. 10517v3 Announce Type: replace Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use.

By Rodrigo Tertulino

benchmarks 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 Machine Learning

Aug 14

CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility

arXiv:2608. 12805v1 Announce Type: new Abstract: Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is constrained by privacy regulation, institutional review, data-use agreements, and the risk of re-identification.

By Akanta Das, Al Amin Farhad, Mrinmoy Sarkar Anto, David Rehkopf, Ayin Vala, Tanmoy Sarkar Pias

benchmarks safety

arXiv Machine Learning

Aug 5

FedCARE: A Multi-Objective Personalised Federated Learning Framework for Smart Healthcare

arXiv:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.

By Rojalini Tripathy, Padmalochan Bera, Shreya Ghosh, Rajkumar Buyya

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