NCBI PubMed Central
open access
Federated Learning with Differential Privacy for Ultrasound Breast Cancer Classification: An Empirical Study.
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
- The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape#1135292
- Federated Learning: The Pioneering Distributed Machine Learning and Privacy-Preserving Data Technology#781755
- Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation#1109921
- Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation#919510
- Advancements in Federated Learning: Models, Methods, and Privacy#177304
Record · ID 232338
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