RAG & Federated Learning

The combination of Retrieval-Augmented Generation (RAG) and Federated Learning (FL) is transforming AI by improving knowledge retrieval and enhancing data privacy. These technologies play a crucial role in making AI systems more efficient, context-aware, and privacy-preserving.Federated Learning (FL) is a decentralized AI training approach that allows models to learn from multiple sources without centralizing sensitive data.

Retrieval-Augmented Generation (RAG) RAG is an AI framework that enhances large language models (LLMs) by integrating external knowledge sources into their response generation process. Unlike standard AI models that rely solely on pre-trained data, RAG retrieves real-time, relevant information before generating answers.AI models are trained locally on user devices or different institutions. Model updates (not raw data) are sent to a central server. The global AI model aggregates insights without exposing personal data.