| name | synpre-fl-synthetic-data-driven-pretraining-integr |
| description | Skill generated from arXiv paper 2607.19524: SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework |
| metadata | {"arxiv":{"id":"2607.19524","title":"SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework","authors":["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"],"published":"2026-07-21","categories":["cs.LG","cs.AI","cs.DC"],"url":"https://arxiv.org/abs/2607.19524","utility":1}} |
SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework
arXiv: 2607.19524
Published: 2026-07-21
Authors: 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
Categories: cs.LG, cs.AI, cs.DC
Utility: 1.00
Key Innovation
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. Synthetic data generation may alleviate data scarcity, yet its integration with federated optimisation has received limited systematic study. We propose SynPre-FL, a unified framework combining high-fidelity synthetic EHR...
Potential Application
This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.LG, cs.AI, cs.DC.
References
