Privacy-Preserving Edge AI Frameworks for Intellectual Property Protection Using Retrieval-Augmented Knowledge Systems

The rapid expansion of artificial intelligence systems and data-driven digital services has intensified concerns regarding the protection of intellectual property in distributed computing environments. Conventional cloud-based AI infrastructures often rely on centralized data storage and processing, which increases the risk of sensitive information exposure, unauthorized knowledge extraction, and potential intellectual property leakage. As organizations increasingly deploy intelligent systems for knowledge-intensive tasks, the need for privacy-preserving architectures that protect proprietary assets while enabling efficient data utilization has become critical. This study proposes a privacy-preserving edge AI framework designed to safeguard intellectual property through the integration of federated learning mechanisms and retrieval-augmented knowledge systems. The proposed architecture enables decentralized processing at edge nodes, allowing sensitive data to remain within local environments while collaborative model training occurs across distributed devices. To further strengthen privacy protection, the framework incorporates differential privacy mechanisms that introduce controlled statistical noise during model updates, preventing the reconstruction of confidential training data. In addition, a retrieval-augmented knowledge layer is integrated to support secure access to distributed knowledge repositories without exposing proprietary datasets. The framework is evaluated using performance metrics such as retrieval accuracy, latency, communication overhead, and privacy leakage risk. Experimental analysis indicates that the proposed approach improves knowledge retrieval efficiency while significantly reducing the likelihood of intellectual property exposure compared with conventional centralized AI architectures. The findings demonstrate that the integration of edge intelligence, privacy-preserving learning, and retrieval-augmented knowledge systems can provide a secure and scalable foundation for protecting intellectual property in modern AI-driven digital ecosystems.