A Distributed Computation Model Based on Federated Learning Integrates
  Heterogeneous models and Consortium Blockchain for Solving Time-Varying
  Problems
Zhihao Hao Guancheng Wang Chunwei Tian Bob Zhang
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.