  Intelligent equipment fault diagnosis based on Federated Transfer Learning
(FTL) attracts considerable attention from both academia and industry. It
allows real-world industrial agents with limited samples to construct a fault
diagnosis model without jeopardizing their raw data privacy. Existing
approaches, however, can neither address the intense sample heterogeneity
caused by different working conditions of practical agents, nor the extreme
fault label scarcity, even zero, of newly deployed equipment. To address these
issues, we present FedLED, the first unsupervised vertical FTL equipment fault
diagnosis method, where knowledge of the unlabeled target domain is further
exploited for effective unsupervised model transfer. Results of extensive
experiments using data of real equipment monitoring demonstrate that FedLED
obviously outperforms SOTA approaches in terms of both diagnosis accuracy (up
to 4.13 times) and generality. We expect our work to inspire further study on
label-free equipment fault diagnosis systematically enhanced by target domain
knowledge.
