A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchain

2025-11-10

AI Summary

Existing centralized RAG systems suffer from high operational costs, privacy vulnerabilities, and—upon decentralization—heterogeneous data source reliability. To address these challenges, this paper proposes the first blockchain-based decentralized RAG framework. Our method integrates (1) a dynamic reliability scoring mechanism that assesses and weights multi-source retrieval in real time based on contribution quality; (2) smart contracts for transparent, tamper-proof, and decentralized management of score generation, updates, and verification; and (3) a hybrid architecture combining decentralized storage, batched state synchronization, and multi-source fusion retrieval using Llama-3B/8B models. Experiments under low-reliability simulated conditions demonstrate a 10.7% improvement in retrieval accuracy and generation quality, performance approaching that of centralized baselines, and a 56% reduction in marginal cost. The system is open-sourced.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Search and Optimization: Distributed Search
• Multiagent Systems: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Blockchains and distributed ledgers
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems

Abstract

Existing retrieval-augmented generation (RAG) systems typically use a centralized architecture, causing a high cost of data collection, integration, and management, as well as privacy concerns. There is a great need for a decentralized RAG system that enables foundation models to utilize information directly from data owners who maintain full control over their sources. However, decentralization brings a challenge: the numerous independent data sources vary significantly in reliability, which can diminish retrieval accuracy and response quality. To address this, our decentralized RAG system has a novel reliability scoring mechanism that dynamically evaluates each source based on the quality of responses it contributes to generate and prioritizes high-quality sources during retrieval. To ensure transparency and trust, the scoring process is securely managed through blockchain-based smart contracts, creating verifiable and tamper-proof reliability records without relying on a central authority. We evaluate our decentralized system with two Llama models (3B and 8B) in two simulated environments where six data sources have different levels of reliability. Our system achieves a +10.7% performance improvement over its centralized counterpart in the real world-like unreliable data environments. Notably, it approaches the upper-bound performance of centralized systems under ideally reliable data environments. The decentralized infrastructure enables secure and trustworthy scoring management, achieving approximately 56% marginal cost savings through batched update operations. Our code and system are open-sourced at github.com/yining610/Reliable-dRAG.

Problem

• Decentralized RAG systems face unreliable data sources affecting accuracy
• Centralized RAG systems have high costs and privacy concerns
• Managing source reliability transparently without central authority is challenging

Innovation

• Decentralized RAG system with blockchain-secured reliability scoring
• Dynamic source evaluation based on response quality contributions
• Smart contracts ensure transparent tamper-proof reliability records