  Operators from various industries have been pushing the adoption of wireless
sensing nodes for industrial monitoring, and such efforts have produced
sizeable condition monitoring datasets that can be used to build diagnosis
algorithms capable of warning maintenance engineers of impending failure or
identifying current system health conditions. However, single operators may not
have sufficiently large fleets of systems or component units to collect
sufficient data to develop data-driven algorithms. Collecting a satisfactory
quantity of fault patterns for safety-critical systems is particularly
difficult due to the rarity of faults. Federated learning (FL) has emerged as a
promising solution to leverage datasets from multiple operators to train a
decentralized asset fault diagnosis model while maintaining data
confidentiality. However, there are still considerable obstacles to overcome
when it comes to optimizing the federation strategy without leaking sensitive
data and addressing the issue of client dataset heterogeneity. This is
particularly prevalent in fault diagnosis applications due to the high
diversity of operating conditions and system configurations. To address these
two challenges, we propose a novel clustering-based FL algorithm where clients
are clustered for federating based on dataset similarity. To quantify dataset
similarity between clients without explicitly sharing data, each client sets
aside a local test dataset and evaluates the other clients' model prediction
accuracy and uncertainty on this test dataset. Clients are then clustered for
FL based on relative prediction accuracy and uncertainty.
