Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation (2504.19101v1)

Abstract: Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution for enhancing the accuracy and credibility of 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. To address these challenges, we regard federated learning (FL) as a highly promising technology for privacy-preserving RAG services. We propose a novel framework called Federated Retrieval-Augmented Generation (FedE4RAG). This framework facilitates collaborative training of client-side RAG retrieval models. The parameters of these models are aggregated and distributed on a central-server, ensuring data privacy without direct sharing of raw data. In FedE4RAG, knowledge distillation is employed for communication between the server and client models. This technique improves the generalization of local RAG retrievers during the federated learning process. Additionally, we apply homomorphic encryption within federated learning to safeguard model parameters and mitigate concerns related to data leakage. Extensive experiments conducted on the real-world dataset have validated the effectiveness of FedE4RAG. The results demonstrate that our proposed framework can markedly enhance the performance of private RAG systems while maintaining robust data privacy protection.

• The paper proposes FedE4RAG, a federated learning framework enabling privacy-preserving Retrieval-Augmented Generation (RAG) systems for private domains by training models collaboratively without sharing sensitive data.
• FedE4RAG utilizes federated learning for collaborative training, knowledge distillation to improve local model generalization, and homomorphic encryption to secure shared model parameters against leakage.
• Experimental validation on a financial dataset demonstrates that FedE4RAG significantly enhances retrieval and generation performance while maintaining robust privacy protection, offering a viable solution for privacy-sensitive industries.

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

In the field of artificial intelligence, Retrieval-Augmented Generation (RAG) techniques have garnered substantial interest due to their capacity to enhance the response quality and credibility of LLMs, especially in domains such as Question and Answer tasks. This enhancement is largely attributed to the integration of external knowledge bases, which supports more informed generative processes. However, the deployment of RAG systems in private domains is hampered by critical concerns related to data privacy and the scarcity of data in private domains. The paper, "Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation," addresses these challenges through the proposition of a federated learning-based framework named Federated Retrieval-Augmented Generation (FedE4RAG).

Framework Design and Methodology

FedE4RAG is an innovative approach that utilizes federated learning to establish a privacy-preserving RAG system. The framework emphasizes the collaborative training of client-side RAG models while maintaining stringent privacy standards. Key components of FedE4RAG include:

• Federated Learning: The core of FedE4RAG leverages federated learning principles to ensure that model parameters are shared across clients without transmitting actual data, thereby protecting sensitive information. Model parameters are aggregated on a central server and redistributed, fostering improved model performance while preserving data confidentiality.
• Knowledge Distillation: To enhance the generalization capabilities of local RAG retrievers during federated learning, knowledge distillation techniques are employed. This aspect facilitates the transfer of distilled knowledge from the server to client models, allowing for improved adaptation and performance in local environments.
• Homomorphic Encryption: Within the federated learning process, homomorphic encryption is applied to secure model parameters and mitigate potential data leakage risks. This cryptographic safeguard ensures that even if data is intercepted during transmission, its confidentiality remains intact.

Experimental Validation

The efficacy of FedE4RAG is empirically validated through extensive experimentation on a real-world dataset concerning financial domains, where privacy concerns are notably stringent. The findings indicate that FedE4RAG markedly enhances the performance metrics compared to traditional paradigms, demonstrating robust privacy protection while achieving superior retrieval quality and generation accuracy.

Implications and Future Directions

The implications of this research are profound, notably for industries that operate under strict data privacy regulations. FedE4RAG presents a viable pathway for leveraging AI in environments where data cannot be openly shared or centralized. Furthermore, the theoretical advancements in federated learning and encryption mechanisms provide foundational insights for developing privacy-preserving models more broadly, beyond RAG systems.

Future research directions suggested by the authors include expanding the FedE4RAG framework to other domains such as legal and healthcare, exploring scalability and efficiency improvements, and deepening the integration of advanced privacy techniques such as differential privacy. Moreover, robustness against potential inference attacks remains a critical area for ongoing development to further safeguard federated systems.

In conclusion, FedE4RAG offers a comprehensive framework for implementing privacy-preserving RAG systems effectively while addressing the data scarcity and security challenges inherent to federated domains. Its contributions to the field of AI and privacy-preserving technologies underline the growing importance and need for solutions in managing and utilizing large-scale LLMs in sensitive industries.