Abstract:Federated learning is an emerging privacy-preserving machine learning paradigm widely applied to the Industrial Internet of Things (IIoT), where multiple clients (e.g. IoT devices) train models locally to formulate a global model under the coordination of a central server. Blockchain has been recently leveraged in IIoT federated learning to maintain data integrity and provide incentives to attract sufficient client data and computation resources for training. However, there is a lack of systematic architecture design for blockchain-based federated learning systems to support methodical development in IIoT. Also, the current solutions do not consider the incentive mechanism design and blockchain scalability. Therefore, in this study, we present a platform architecture of blockchain-based federated learning systems in IIoT, where each client hosts a server for local model training and manages a full blockchain node. For verifiable integrity of client data in a scalable way, each client server periodically creates a Merkle tree in which each leaf node represents a client data record and stores the tree root on a blockchain. To encourage clients to participate in federated learning, an on-chain incentive mechanism is designed based on the size of client data used in local model training to accurately and timely calculate each client’s contribution. A prototype of the proposed architecture is implemented with our industry partner and evaluated in terms of feasibility, accuracy and performance. The results show that the approach ensures data integrity and has satisfactory prediction accuracy, and performance.