中国科学院自动化研究所机构知识库

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 Since the existing trust-based collaboration models are no longer applicable for the large-scale distributed collaboration among trustless machines in open and dynamic environments, this article proposes a novel decentralized AI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.