Distributed Edge Intelligence for Patent Analytics: Secure RAG-Driven Knowledge Retrieval in Decentralized Innovation Networks

Abstract

The rapid expansion of global patent repositories and the increasing complexity of decentralized innovation ecosystems have
created significant challenges for traditional patent analytics systems. Centralized architectures often suffer from high latency,
limited scalability, and critical privacy risks, particularly when handling sensitive intellectual property data across distributed
environments. This study proposes a novel distributed edge intelligence framework that integrates edge computing, federated
learning, blockchain technology, and retrieval-augmented generation to enable secure, scalable, and real-time patent knowledge
retrieval. The framework leverages edge nodes for localized data processing, reducing communication overhead and latency,
while federated learning ensures collaborative model training without exposing proprietary datasets. A blockchain-based trust
layer provides secure validation and immutable record-keeping, enhancing transparency and data integrity across decentralized
networks. Furthermore, the integration of a retrieval-augmented generation mechanism enables context-aware knowledge
extraction, significantly improving the accuracy and relevance of patent insights. The proposed system is evaluated through a
distributed experimental setup, demonstrating substantial improvements in latency reduction, retrieval accuracy, throughput
performance, and security robustness compared to conventional centralized systems. The findings highlight the potential of
combining distributed intelligence and advanced language models to transform patent analytics into a more adaptive, secure,
and efficient process. This work contributes a unified architectural paradigm for next-generation innovation intelligence systems.