Semantic Scholar
metadata only
PQSF: post-quantum secure privacy-preserving federated learning
In federated learning, secret sharing is a key technology to maintain the privacy of participants’ local models. Moreover, with the rapid development of quantum computers, existing federated learning privacy protection schemes based on secret sharing will no longer be able to guarantee the data secu
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
- Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners#1354075
- Blockchain-enabled verifiable federated learning with multi-dimensional incentive and dynamic consensus for privacy-sensitive domains#309958
- FLAI Protocol: Decentralized Federated Learning with On-Chain Rewards and sPEG-Based Contribution Scoring#1342760
- FLAI Protocol: Decentralized Federated Learning with On-Chain Rewards and sPEG-Based Contribution Scoring#1318970
- Making the most of leftovers: Improved privacy amplification for quantum key distribution#1142907
Record · ID 1502222
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