  Machine learning algorithms are undoubtedly one of the most popular
algorithms in recent years, and neural networks have demonstrated unprecedented
precision. In daily life, different communities may have different user
characteristics, which also means that training a strong model requires the
union of different communities, so the privacy issue needs to be solved
urgently. Federated learning is a popular privacy solution, each community does
not need to expose specific data, but only needs to upload sub-models to the
coordination server to train more powerful models. However, federated learning
also has some problems, such as the security and fairness of the coordination
server. A proven solution to the problem is a decentralized implementation of
federated learning. In this paper, we apply decentralized tools such as
blockchain and consensus algorithms to design a support system that supports
the decentralized operation of federated learning in an alliance environment,
involving the exploration of incentives, security, fairness and other issues.
Finally, we experimentally verify the performance of our system, the effect of
federated learning, and the availability of privacy protection.
