The recurrent neural network has been greatly developed for effectively solving time-varying problems corresponding to complex environments. However, limited by the way of centralized processing, the model performance is greatly affected by factors like the silos problems of the models and data in reality. Therefore, the emergence of distributed artificial intelligence such as federated learning (FL) makes it possible for the dynamic aggregation among models. However, the integration process of FL is still server-dependent, which may cause a great risk to the overall model. Also, it only allows collaboration between homogeneous models, and does not have a good solution for the interaction between heterogeneous models. Therefore, we propose a Distributed Computation Model (DCM) based on the consortium blockchain network to improve the credibility of the overall model and effective coordination among heterogeneous models. In addition, a Distributed Hierarchical Integration (DHI) algorithm is also designed for the global solution process. Within a group, permissioned nodes collect the local models' results from different permissionless nodes and then sends the aggregated results back to all the permissionless nodes to regularize the processing of the local models. After the iteration is completed, the secondary integration of the local results will be performed between permission nodes to obtain the global results. In the experiments, we verify the efficiency of DCM, where the results show that the proposed model outperforms many state-of-the-art models based on a federated learning framework.

翻译：循环神经网络在处理复杂环境下的时变问题方面取得了显著进展。然而，受限于集中式处理方式，模型性能易受现实中的数据孤岛和模型孤岛等因素影响。联邦学习等分布式人工智能技术的出现使得模型间的动态聚合成为可能。但联邦学习的集成过程仍然依赖于中心服务器，这会对整体模型构成重大风险。此外，该框架仅允许同构模型间的协作，对异构模型间的交互缺乏良好解决方案。为此，我们提出一种基于联盟链网络的分布式计算模型（DCM），以提升整体模型的可信度并实现异构模型间的有效协调。同时，针对全局求解过程设计了分布式分层集成（DHI）算法。在每个分组内，授权节点收集来自不同非授权节点的本地模型结果，将聚合结果回传至所有非授权节点以规范本地模型处理。迭代完成后，授权节点间将对本地结果进行二次集成以获得全局结果。实验验证了DCM的有效性，结果表明所提模型在联邦学习框架下优于多种当前最优模型。