SoK: Decentralized AI (DeAI) (2411.17461v3)

Abstract: Centralization enhances the efficiency of 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 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.

• The paper introduces a taxonomy that classifies decentralized AI protocols across the AI model lifecycle to clarify design differences and similarities.
• The paper demonstrates how blockchain functionalities like auditability and token-based incentives can enhance AI security, transparency, and trust.
• The paper identifies research gaps in scalable privacy solutions and efficient incentive mechanisms, marking key directions for further development.

A Systematization of Knowledge for Blockchain-Based Decentralized AI

The paper "SoK: Decentralized AI (DeAI)" presents a detailed analysis of blockchain-based decentralized artificial intelligence (DeAI) systems. It highlights the challenges posed by centralized AI architectures such as security vulnerabilities, privacy concerns, biases, and scalability barriers. The authors propose blockchain technology as a potential solution to these issues, aiming to create secure, transparent, and decentralized AI systems.

Central Thesis and Contributions

The central thesis of the paper is that the convergence of blockchain and AI technologies holds promise for overcoming the limitations of centralized systems. The authors systematize existing knowledge in the field, categorizing DeAI protocols based on stages of the AI model lifecycle. They further outline a comprehensive taxonomy that details the functionalities of blockchain in addressing issues within AI processes.

Key Contributions:

• Taxonomy of DeAI Protocols: The paper lays out a taxonomy to classify DeAI protocols based on the AI model lifecycle, ranging from task proposal to post-training phases. This classification facilitates understanding the landscape of DeAI solutions and distinguishes their similarities and differences.
• Blockchain Functionalities: The authors analyze how blockchain can enhance security, transparency, and trustworthiness in AI implementations through functionalities such as public reference, auditability, and fair incentives.
• Identification of Research Gaps: The paper highlights research gaps in the development of DeAI systems, including the need for efficient, scalable privacy solutions and enhanced incentive mechanisms for data sharing and model training.

Insights and Findings

The authors illuminate several insights critical to advancing DeAI:

1. Incentive Mechanisms: Effective incentive mechanisms, crucial for fostering collaboration, are achievable through tokenomics and staking models, aligning participant objectives with the network’s goals.
2. Privacy and Security: Zero-knowledge proofs (ZKP) and multi-party computation (MPC) are identified as promising techniques to ensure data privacy and security while maintaining the integrity of decentralized AI processes.
3. Decentralization and Trust: Blockchain’s decentralized network can eliminate single points of failure, increasing resilience against attacks and technical disruptions, thus fostering trust among stakeholders.

Implications and Future Directions

The research underscores the potential of blockchain-based DeAI to significantly enhance the robustness of AI systems. By decentralizing AI processes, these integrated systems could offer novel solutions to privacy challenges and ensure transparent operations through immutable ledgers.

Theoretical Implications:

• Extended Applicability of Blockchain: The application of blockchain in AI could redefine trust mechanisms in distributed systems, influencing the design of future AI architectures.
• Adaptation to Multimodal Environments: Integrating blockchain in AI may require adaption to various data modalities and distributed computing environments, demanding novel algorithms and consensus mechanisms.

Practical Implications:

• Operational Efficiency: By deploying computation power and data across decentralized nodes, operational bottlenecks of centralized AI systems can be alleviated, paving the way for more widespread adoption of AI technologies.
• Data Privacy: Protecting data privacy without sacrificing analytic capabilities is increasingly essential, especially in sectors like healthcare and finance.

Conclusion

The paper provides a comprehensive overview of blockchain-based decentralized AI systems, emphasizing a coherent framework and identifying strategic areas for future research. By categorizing existing protocols and examining blockchain’s role in decentralizing AI, the authors lay the groundwork for advancing this innovative intersection of technologies. Their findings suggest that bridging AI and blockchain could redefine how intelligent systems are structured, fostering a more equitable and secure digital ecosystem for AI applications.