EduBot: Integrating Federated Learning and RAG for Educational Document Analysis

Abstract

There is a growing demand for intelligent and secure document management systems in the education sector, driven by the exponential growth of digital content. Nevertheless, classic centralized machine learning methods pose major privacy challenges. In this study, we present EduBot, a decentralized intelligent framework for education that integrates Federated Learning (FL) with Retrieval-Augmented Generation (RAG) to ensure privacy and deliver an advanced user experience for content interaction. EduBot introduces a dual-mechanism architecture that enables users to interact with their private documents in real time via a RAG-based conversational interface while also allowing local fine-tuning of a Low-Rank Adaptation (LoRA) adapter on the same data. The masked adapter updates are aggregated on a central server using the Federated Averaging method, enabling collaborative learning without exposing any private documents. The system was evaluated using 29 textbooks published by the Turkish Ministry of National Education (MoNE) and 1318 validated questions. Federated Learning (FL) produced a single unified model that could be deployed across all three domains without sharing the underlying documents. The RAG system retrieved the correct source page among the top five results for 93.3% of the questions. When combined with RAG, the FL model achieved the highest answer quality among the Qwen2.5-1.5B-based models, outperforming the centralized model (token F1 of 0.067 and 0.053, respectively). Overall, our study validates the feasibility of developing privacy-preserving AI assistants that can support educational ecosystems without centralizing sensitive data.