OpenAlex
metadata only
CC BY 4.0
Privacy-Preserving Data Mining Methods Metrics and Applications in Healthcare Informatics
Their fields have a profound interest in PPDM as a technical progress in health informatics, balancing the need to extract valuable information for clinical decisions while preserving sensitive data. Classic federated learning (FL) models have various limitations like intensive computational loads a
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
- Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data#984207
- Privacy-preserving machine learning: a review of federated learning techniques and applications#964892
- Federated Learning: The Pioneering Distributed Machine Learning and Privacy-Preserving Data Technology#781755
- A privacy-enhanced framework for collaborative Big Data analysis in healthcare using adaptive federated learning aggregation#341897
- FL-SMPC++: A robust framework for privacy-preserving federated learning#364028
Record · ID 1178338
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