In recent years, the construction of Space–Ground Integrated Network has been accelerated, connecting different types of networks in remote regions. The various devices are connected together, so that data that was difficult to communicate before can be used to train particular models together, giving birth to new service models. Privacy issues, however, remain a substantial concern affecting data sharing among multiple parties. Cooperative training methods such as federated learning usually require a centralized aggregator to aggregate the dispersed sub-models. In general, various privacy-preserving methods assume the aggregator as an honest-but-curious (HBC) role and cannot guarantee that the program can be executed correctly. In this paper, we propose a blockchain-based collaborative training method that uses the decentralized accounting technology of the blockchain to solve the trust problem between different participants. Through the anti-repudiation nature of the blockchain, it is ensured that the aggregation of the model is executed correctly. We designed a function encryption-based privacy preserving method in which the aggregator can only obtain the results of the aggregation model, and cannot access the models uploaded to the blockchain from other participants. Subsequently, a prototype system based on blockchain is developed to analyze and evaluate the time consumption of our proposed cooperative training method and function encryption module. The result of our experiments shows the feasibility of our cooperative training model.

近年来，加速了空间地面综合网络的建设，连接了偏远地区的不同类型的网络。各种设备连接在一起，因此以前难以通信的数据可用于一起训练特定模型，从而催生了新的服务模型。但是，隐私问题仍然是影响多方数据共享的重大问题。诸如联合学习之类的合作培训方法通常需要集中的聚合器来聚合分散的子模型。通常，各种保护隐私的方法都将聚合器假定为诚实但好奇（HBC）角色，并且不能保证程序可以正确执行。在本文中，我们提出了一种基于区块链的协作培训方法，该方法使用区块链的去中心化会计技术来解决不同参与者之间的信任问题。通过区块链的抗抵赖特性，可以确保正确执行模型的聚合。我们设计了一种基于函数加密的隐私保护方法，其中聚合器只能获取聚合模型的结果，而不能访问其他参与者上传到区块链的模型。随后，开发了一个基于区块链的原型系统，以分析和评估我们提出的协作训练方法和功能加密模块的时间消耗。我们的实验结果表明了我们合作训练模型的可行性。