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Recent research in Internet of things has been widely applied for industrialpractices, fostering the exponential growth of data and connected devices.Henceforth, data-driven AI models would be accessed by different partiesthrough certain data-sharing policies. However, most of the current trainingprocedures rely on the centralized data-collection strategy and a singlecomputational server. However, such a centralized scheme may lead to manyissues. Customer data stored in a centralized database may be tampered with sothe provenance and authenticity of data cannot be justified. Once theaforementioned security concerns occur, the credibility of the trained AImodels would be questionable and even unfavorable outcomes might be produced atthe test stage. Lately, blockchain and AI, the two core technologies inIndustry 4.0 and Web 3.0, have been explored to facilitate the decentralized AItraining strategy. To serve on this very purpose, we propose a new systemarchitecture called APPFLChain, namely an integrated architecture of aHyperledger Fabric-based blockchain and a federated-learning paradigm. Ourproposed new system allows different parties to jointly train AI models andtheir customers or stakeholders are connected by a consortium blockchain-basednetwork. Our new system can maintain a high degree of security and privacy asusers do not need to share sensitive personal information to the server. Fornumerical evaluation, we simulate a real-world scenario to illustrate the wholeoperational process of APPFLChain. Simulation results show that takingadvantage of the characteristics of consortium blockchain and federatedlearning, APPFLChain can demonstrate favorable properties includinguntamperability, traceability, privacy protection, and reliabledecision-making.

APPFLChain: A Privacy Protection Distributed Artificial-Intelligence Architecture Based on Federated Learning and Consortium Blockchain