FedE4RAG_Dataset

This is the dataset of the paper Privacy-Preserving Federal Embedding Learning for Localized Retrieval-Augmented Generation.FedE4RAG addresses data scarcity and privacy challenges in private RAG systems. It uses federated learning (FL) to collaboratively train client-side RAG retrieval models, keeping raw data localized. The framework employs knowledge distillation for effective server-client communication and homomorphic encryption to enhance parameter privacy. FedE4RAG aims to boost the performance of localized RAG retrievers by leveraging diverse client insights securely, balancing data utility and confidentiality, particularly demonstrated in sensitive domains like finance.