Learning Markets: An AI Collaboration Framework Based on Blockchain and Smart Contracts

【Author】 Ouyang, Liwei; Yuan, Yong; Wang, Fei-Yue

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Artificial intelligence (AI) has been witnessed to provide valuable solutions to all walks of life. However, data island and computing resources limitations in the centralized AI architectures have increased their technical barriers, and thus distributed AI collaboration in data, models, and resources has attracted intensive research interests. Since the existing trust-based collaboration models are no longer applicable for the large-scale distributed collaboration among trustless machines in open and dynamic environments, this article proposes a novel decentralized AI collaboration framework, i.e., learning markets (LM), in which blockchain provides a trustless environment for collaboration and transaction, while smart contracts serve as software-defined agents to encapsulate and process scalable collaboration relationships and market mechanisms. LM can not only help those participants without mutual trust realize collaborative mining with dynamic and quantitative rewards but also build an AI market with natural auditability and traceability for trading trusted and verified models. We implement and comprehensively analyze LM based on the Ethereum interplenary file system platform (IPFS), and the results prove that it has advantages in collaboration fairness, transparency, security, decentralization and universality. Based on our collaboration framework, distributed AI contributors are expected to cooperate and complete those learning tasks that cannot be done previously due to lack of complete data, sufficient computing resources and state-of-the-art models.

【Keywords】Collaboration; Artificial intelligence; Data models; Computational modeling; Smart contracts; Blockchain; Distributed databases; Artificial intelligence (AI) collaboration; blockchain; ensemble learning; federated learning (FL); smart contracts

【发表时间】2022 AUG 15

【收录时间】2022-08-28

【文献类型】理论模型

【主题类别】

区块链技术-核心技术-智能合约