arXiv (All)
open access
Federated Art Chase: Privacy Leakage and Data Poisoning in Federated Fine-Tuning
Federated learning (FL) is increasingly used to adapt a pretrained, centrally fine-tuned model to data that remain on user devices, beyond training a model from scratch. In this report, prepared for the 4th Federated Machine Learning International Summer School, we study the privacy and integrity of
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 Art Chase: Privacy Leakage and Data Poisoning in Federated Fine-Tuning#1348768
- Enhanced Security and Privacy via Fragmented Federated Learning#400577
- A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency#177298
- Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning#177276
- Privacy and Trust Redefined in Federated Machine Learning#400581
Record · ID 1356883
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