OpenAlex
metadata only
Systematic review of privacy-preserving Federated Learning in decentralized healthcare systems
Federated Learning (FL) has become a promising method for training machine learning models while protecting patient privacy. This systematic review examines the use of privacy-preserving techniques in FL within decentralized healthcare systems. It compares existing methods such as Differential Priva
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
- Privacy-preserving machine learning: a review of federated learning techniques and applications#964892
- Turning Privacy-preserving Mechanisms against Federated Learning#177333
- Federated Learning with Privacy-preserving and Model IP-right-protection#177289
- Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning#177320
- RP-IoMT: A Robust and Provable Framework for Federated Learning Privacy-Preserving Intelligence in Healthcare IoMT#341889
Record · ID 964797
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