  Device failure detection is one of most essential problems in industrial
internet of things (IIoT). However, in conventional IIoT device failure
detection, client devices need to upload raw data to the central server for
model training, which might lead to disclosure of sensitive business data.
Therefore, in this paper, to ensure client data privacy, we propose a
blockchain-based federated learning approach for device failure detection in
IIoT. First, we present a platform architecture of blockchain-based federated
learning systems for failure detection in IIoT, which enables verifiable
integrity of client data. In the architecture, each client periodically creates
a Merkle tree in which each leaf node represents a client data record, and
stores the tree root on a blockchain. Further, to address the data
heterogeneity issue in IIoT failure detection, we propose a novel centroid
distance weighted federated averaging (CDW\_FedAvg) algorithm taking into
account the distance between positive class and negative class of each client
dataset. In addition, to motivate clients to participate in federated learning,
a smart contact based incentive mechanism is designed depending on the size and
the centroid distance of client data used in local model training. 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 is feasible, and has satisfactory accuracy and performance.
