Semantic Scholar
metadata only
Secure and Flexible Privacy-Preserving Federated Learning Based on Multi-Key Fully Homomorphic Encryption
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
- FL-EASGD: Federated Learning Privacy Security Method Based on Homomorphic Encryption#341886
- Exploring Homomorphic Encryption and Differential Privacy Techniques towards Secure Federated Learning Paradigm#177279
- Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning#177287
- Federated Learning and Blockchain Integration for Privacy Protection in the Internet of Things: Challenges and Solutions#177332
- Towards federated learning for face recognition: a comprehensive review of secure and privacy-preserving techniques and prospective research.#975642
Record · ID 341908
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