🤖 AI Summary
This paper addresses critical limitations of centralized AI—particularly proprietary large language models—including single-point failure, data bias, privacy leakage, and poor scalability. To this end, it proposes a blockchain-based Decentralized Artificial Intelligence (DeAI) framework. Methodologically, the study introduces the first taxonomy of DeAI protocols spanning the entire AI model lifecycle, systematically analyzing blockchain’s functional mappings in data contribution, model training, inference services, and governance. It integrates core technologies including consensus mechanisms, smart contracts, zero-knowledge proofs, decentralized storage, and incentive design. The contribution is a comprehensive DeAI analytical framework—the first of its kind—that comparatively characterizes existing approaches, identifies key research gaps, and outlines evolutionary pathways. This work provides both theoretical foundations and practical guidelines for building next-generation AI infrastructure that is transparent, secure, trustworthy, and equitably incentivized.
📝 Abstract
Centralization enhances the efficiency of Artificial Intelligence (AI), but it also brings critical challenges, such as single points of failure, inherent biases, data privacy concerns, and scalability issues, for AI systems. These problems are especially common in closed-source large language models (LLMs), where user data is collected and used with full transparency. To address these issues, blockchain-based decentralized AI (DeAI) has been introduced. DeAI leverages the strengths of blockchain technologies to enhance the transparency, security, decentralization, as well as trustworthiness of AI systems. Although DeAI has been widely developed in industry, a comprehensive understanding of state-of-the-art practical DeAI solutions is still lacking. In this work, we present a Systematization of Knowledge (SoK) for blockchain-based DeAI solutions. We propose a taxonomy to classify existing DeAI protocols based on the model lifecycle. Based on this taxonomy, we provide a structured way to clarify the landscape of DeAI protocols and identify their similarities and differences. Specifically, we analyze the functionalities of blockchain in DeAI, investigate how blockchain features contribute to enhancing the security, transparency, and trustworthiness of AI processes, and also ensure fair incentives for AI data and model contributors. In addition, we provide key insights and research gaps in developing DeAI protocols for future research.