符合法律法规、数据伦理约束的大规模数据交换是数据要素化重要的基础条件。联邦学习作为一种新兴技术解决了数据交换的隐私问题，得到学术界的高度关注。但联邦学习的具体方法尚不成熟，离大规模应用还有距离。区块链和联邦学习的协作简化了数据分布场景、提供了学习过程的追溯、基于零知识证明方法实现了数据交易的“事先评估”。基于区块链的信任媒介作用，可以通过区块链系统记录训练参数、模型数据、数据调用过程等，实现多方合作的可信隐私计算平台。在不暴露具体数据的前提下，通过神经网络的模型、梯度等数据共享，实现数据蕴含的知识价值传递，从而打破既有条件下的数据孤岛，构建数据价值链条。
Large scale data exchange in line with laws and regulations and data ethical constraints is an important conditional basis for making data as a production factor. As an emerging technology，federated learning solves the privacy problem of data exchange，which has been highly concerned by the academic community. However，its specific method is not mature，and it is far from large-scale application. The cooperation of blockchain and federated learning simplifies the data distribution scenario，provides the traceability of learning process，and realizes the “pre-evaluation” of data transaction. Based on the role of trust media of blockchain，the trusted privacy computing platform with multi-party cooperation can be realized by recording the training parameters，model data，data call process，etc. On the premise of not exposing specific data，through the data sharing of neural network model and gradient，the knowledge value contained in the data can be transferred to break the existing data island and build the data value chain.
