SoK: Blockchain-Based Decentralized AI (DeAI)
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917255611154432 |
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| author | Lui, Elizabeth Sun, Rui Shah, Vatsal Xiong, Xihan Sun, Jiahao Crapis, Davide Knottenbelt, William Wang, Zhipeng |
| author_facet | Lui, Elizabeth Sun, Rui Shah, Vatsal Xiong, Xihan Sun, Jiahao Crapis, Davide Knottenbelt, William Wang, Zhipeng |
| contents | Centralization enhances the efficiency of Artificial Intelligence (AI) but also introduces critical challenges, including single points of failure, inherent biases, data privacy risks, and scalability limitations. To address these issues, blockchain-based Decentralized Artificial Intelligence (DeAI) has emerged as a promising paradigm that leverages decentralization and transparency to improve the trustworthiness of AI systems. Despite rapid adoption in industry, the academic community lacks a systematic analysis of DeAI's technical foundations, opportunities, and challenges. This work presents the first Systematization of Knowledge (SoK) on DeAI, offering a formal definition, a taxonomy of existing solutions based on the AI lifecycle, and an in-depth investigation of the roles of blockchain in enabling secure and incentive-compatible collaboration. We further review security risks across the DeAI lifecycle and empirically evaluate representative mitigation techniques. Finally, we highlight open research challenges and future directions for advancing blockchain-based DeAI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17461 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SoK: Blockchain-Based Decentralized AI (DeAI) Lui, Elizabeth Sun, Rui Shah, Vatsal Xiong, Xihan Sun, Jiahao Crapis, Davide Knottenbelt, William Wang, Zhipeng Machine Learning Artificial Intelligence Cryptography and Security Centralization enhances the efficiency of Artificial Intelligence (AI) but also introduces critical challenges, including single points of failure, inherent biases, data privacy risks, and scalability limitations. To address these issues, blockchain-based Decentralized Artificial Intelligence (DeAI) has emerged as a promising paradigm that leverages decentralization and transparency to improve the trustworthiness of AI systems. Despite rapid adoption in industry, the academic community lacks a systematic analysis of DeAI's technical foundations, opportunities, and challenges. This work presents the first Systematization of Knowledge (SoK) on DeAI, offering a formal definition, a taxonomy of existing solutions based on the AI lifecycle, and an in-depth investigation of the roles of blockchain in enabling secure and incentive-compatible collaboration. We further review security risks across the DeAI lifecycle and empirically evaluate representative mitigation techniques. Finally, we highlight open research challenges and future directions for advancing blockchain-based DeAI. |
| title | SoK: Blockchain-Based Decentralized AI (DeAI) |
| topic | Machine Learning Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2411.17461 |