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The recurrent neural network has been greatly developed for effectivelysolving time-varying problems corresponding to complex environments. However,limited by the way of centralized processing, the model performance is greatlyaffected by factors like the silos problems of the models and data in reality.Therefore, the emergence of distributed artificial intelligence such asfederated learning (FL) makes it possible for the dynamic aggregation amongmodels. However, the integration process of FL is still server-dependent, whichmay cause a great risk to the overall model. Also, it only allows collaborationbetween homogeneous models, and does not have a good solution for theinteraction between heterogeneous models. Therefore, we propose a DistributedComputation Model (DCM) based on the consortium blockchain network to improvethe credibility of the overall model and effective coordination amongheterogeneous 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 differentpermissionless nodes and then sends the aggregated results back to all thepermissionless nodes to regularize the processing of the local models. Afterthe iteration is completed, the secondary integration of the local results willbe performed between permission nodes to obtain the global results. In theexperiments, we verify the efficiency of DCM, where the results show that theproposed model outperforms many state-of-the-art models based on a federatedlearning framework.

A Distributed Computation Model Based on Federated Learning Integrates  Heterogeneous models and Consortium Blockchain for Solving Time-Varying  Problems