OpenAlex
metadata only
Federated Learning: The Pioneering Distributed Machine Learning and Privacy-Preserving Data Technology
Federated learning (pioneered by Google) is a new class of machine learning models trained on distributed data sets, and equally important, a key privacy-preserving data technology. The contribution of this article is to place it in perspective to other data science technologies.
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
- Privacy and Trust Redefined in Federated Machine Learning#400581
- From distributed machine learning to federated learning: In the view of data privacy and security#328785
- Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation#1109921
- Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation#919510
Record · ID 781755
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