arXiv (All)
open access
Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
Federated learning enables collaborative training without sharing patient-level data, but most studies remain simulations. Based on five requirements derived from the literature, we analyzed 14 FL frameworks and found that none fully satisfied these requirements. We present FL-Net, a novel federated
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning#978346
- FEDI-CODE: A federated and causally-informed framework for dementia risk prediction using multi-site patient data.#312339
- Cross-silo federated learning for predicting successful mechanical ventilation weaning across 5 intensive care unit databases: retrospective database analysis / Seyedmostafa Sheikhalishahi, Mathias Kaspar, Johanna Schwinn, Matthaeus Morhart, Philipp Simon, Ludwig Christian Hinske#661753
- Federated analytics for non-communicable disease surveillance in the European health data space: a scoping review and conceptual framework.#659653
- FLOSS: Federated Learning with Opt-Out and Straggler Support#158756
Record · ID 1034452
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive