A Blockchain-Based Collaborative Training Method For Multi-Party Data Sharing

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.