With the development of big data, data sharing has become a hot topic. According to the previous research on data sharing, there is a problem with regard to how to design an effective incentive mechanism to make users willing to share data. First, we integrate the incentives based on reputation and payment and introduce “credibility coins” as a cryptocurrency for data-sharing transactions, to encourage users to participate honestly in the data-sharing process based on federated learning. Second, we propose a dynamic incentive model based on the evolutionary game theory to model the game process of users in data sharing and analyze the stability of their strategies. Finally, based on the results of this analysis, we use the blockchain-based smart contract technology to dynamically adjust the participation benefits of users under different conditions in order to promote users to join consortium blockchains more often and steadily to participate in model training for federated learning and obtain better model accuracy. Our work is the first to apply the evolutionary game theory to the study of incentives in federated learning, and plays a leading role in the study of incentives in federated learning. Experimental simulation validation shows that our DIM-DS model can adequately motivate users to participate in the collaborative task of data sharing and maintain stability. The model can maximize the effectiveness of the federated learning model.

中文翻译：

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DIM-DS：基于智能合约和演化博弈论的联邦学习数据共享动态激励模型

随着大数据的发展，数据共享成为热门话题。根据以往对数据共享的研究，存在一个问题是如何设计有效的激励机制，使用户愿意共享数据。首先，我们整合基于声誉和支付的激励机制，引入“信用币”作为数据共享交易的加密货币，鼓励用户诚实参与基于联邦学习的数据共享过程。其次，我们提出了一种基于演化博弈论的动态激励模型，对用户在数据共享中的博弈过程进行建模，并分析其策略的稳定性。最后，根据这个分析的结果，我们利用基于区块链的智能合约技术，动态调整用户在不同情况下的参与收益，以促进用户更频繁、更稳定地加入联盟链，参与联邦学习的模型训练，获得更好的模型准确率。我们的工作率先将演化博弈论应用于联邦学习中的激励研究，在联邦学习中的激励研究中发挥了主导作用。实验仿真验证表明，我们的DIM-DS模型可以充分激励用户参与数据共享的协作任务并保持稳定性。该模型可以最大限度地发挥联邦学习模型的有效性。