Semantic Scholar
metadata only
Advancing Power System Services With Privacy-Preserving Federated Learning Techniques: A Review
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
- Federated deep learning for distributed intrusion detection and privacy preservation in power networks#355768
- 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
- A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection#177250
- A Standardized Framework for Machine Learning in Power System Protection#471233
Record · ID 177263
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