Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation

Understand

Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution for enhancing the accuracy and credibility of Large Language Models (LLMs), particularly in Question & Answer tasks.

• This is achieved by incorporating proprietary and private data from integrated databases.
• However, private RAG systems face significant challenges due to the scarcity of private domain data and critical data privacy issues.
• These obstacles impede the deployment of private RAG systems, as developing privacy-preserving RAG systems requires a delicate balance between data security and data availability.