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Conference Paper Design Issues in Implementation of Decentralized AI-data commons Framework
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Authors
Young-Ho Suh, Sungpil Woo, Boyun Eom, Dong-Hwan Park, Sunhwan Lim, Chan-Won Park
Issue Date
2023-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2023, pp.1-3
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC58733.2023.10392898
Abstract
In this paper, we propose a system architecture for AI-data commons. An AI-data commons system can form a shared data economy among various stakeholders such as data owners, data providers, and AI/data consumers, computing providers and problem solvers. In the AI-Data Commons ecosystem, various AI modules and data are shared/distributed in a way that ensures the sovereignty and privacy of AI-data owners through decentralized interactions among stakeholders.
KSP Keywords
Data Commons, Data Providers, Data consumers, Data economy, Design issues, Shared data, System architecture