  Recent research in Internet of things has been widely applied for industrial
practices, fostering the exponential growth of data and connected devices.
Henceforth, data-driven AI models would be accessed by different parties
through certain data-sharing policies. However, most of the current training
procedures rely on the centralized data-collection strategy and a single
computational server. However, such a centralized scheme may lead to many
issues. Customer data stored in a centralized database may be tampered with so
the provenance and authenticity of data cannot be justified. Once the
aforementioned security concerns occur, the credibility of the trained AI
models would be questionable and even unfavorable outcomes might be produced at
the test stage. Lately, blockchain and AI, the two core technologies in
Industry 4.0 and Web 3.0, have been explored to facilitate the decentralized AI
training strategy. To serve on this very purpose, we propose a new system
architecture called APPFLChain, namely an integrated architecture of a
Hyperledger Fabric-based blockchain and a federated-learning paradigm. Our
proposed new system allows different parties to jointly train AI models and
their customers or stakeholders are connected by a consortium blockchain-based
network. Our new system can maintain a high degree of security and privacy as
users do not need to share sensitive personal information to the server. For
numerical evaluation, we simulate a real-world scenario to illustrate the whole
operational process of APPFLChain. Simulation results show that taking
advantage of the characteristics of consortium blockchain and federated
learning, APPFLChain can demonstrate favorable properties including
untamperability, traceability, privacy protection, and reliable
decision-making.
