Although different infrastructures are used to create the blockchain
structure in the studies, Hyperledger Fabric infrastructure is generally
preferred. In addition, data integrity, data security, tamper resistance
and distributed structure have been successfully carried out in many
studies.

In this study, a four-layer architecture is presented to increase the
learning success of data processing systems in distributed locations, to
achieve higher accuracy and to create a secure storage. The data from
the nodes are incrementally collected to the central server and trained
with the LSTM deep learning algorithm. The weights containing the
learning results are transferred to the decision layer and all node
devices are used. In addition, post-training data is safely stored in a
blockchain-based storage system. Thus, learning capabilities have been
transferred to all devices. In the tests and evaluations, each node
showed high learning performance in new data, apart from its own data
diversity. As a result, it is aimed to solve the problems experienced in
cases where the data obtained from a location, the number of data and
the diversity are insufficient, with the central architecture. In
particular, it is foreseen that distributed systems will be processed
securely and with higher accuracy, and that IoT systems will be used
widely and effectively.