  Artificial intelligence (AI)-empowered industrial fault diagnostics is
important in ensuring the safe operation of industrial applications. Since
complex industrial systems often involve multiple industrial plants (possibly
belonging to different companies or subsidiaries) with sensitive data collected
and stored in a distributed manner, collaborative fault diagnostic model
training often needs to leverage federated learning (FL). As the scale of the
industrial fault diagnostic models are often large and communication channels
in such systems are often not exclusively used for FL model training, existing
deployed FL model training frameworks cannot train such models efficiently
across multiple institutions. In this paper, we report our experience
developing and deploying the Federated Opportunistic Block Dropout (FEDOBD)
approach for industrial fault diagnostic model training. By decomposing
large-scale models into semantic blocks and enabling FL participants to
opportunistically upload selected important blocks in a quantized manner, it
significantly reduces the communication overhead while maintaining model
performance. Since its deployment in ENN Group in February 2022, FEDOBD has
served two coal chemical plants across two cities in China to build industrial
fault prediction models. It helped the company reduce the training
communication overhead by over 70% compared to its previous AI Engine, while
maintaining model performance at over 85% test F1 score. To our knowledge, it
is the first successfully deployed dropout-based FL approach.

Machine Learning