The synergy between Federated Learning and blockchain has been considered promising; however, the computationally intensive nature of contribution measurement conflicts with the strict computation and storage limits of blockchain systems. We propose a novel concept to decentralize the AI training process using blockchain technology and Multi-task Peer Prediction. By leveraging smart contracts and cryptocurrencies to incentivize contributions to the training process, we aim to harness the mutual benefits of AI and blockchain. We discuss the advantages and limitations of our design.

翻译：联邦学习与区块链的协同被视为具有广阔前景，然而贡献评估的计算密集特性与区块链系统严格的计算和存储限制存在冲突。我们提出一种创新概念，通过区块链技术与多任务同伴预测实现人工智能训练过程的去中心化。利用智能合约和加密货币激励对训练过程的贡献，我们旨在发挥人工智能与区块链的互补优势。本文探讨了该设计的优势与局限性。