  Enabled by the increasing availability of sensor data monitored from
production machinery, condition monitoring and predictive maintenance methods
are key pillars for an efficient and robust manufacturing production cycle in
the Industrial Internet of Things. The employment of machine learning models to
detect and predict deteriorating behavior by analyzing a variety of data
collected across several industrial environments shows promising results in
recent works, yet also often requires transferring the sensor data to
centralized servers located in the cloud. Moreover, although collaborating and
sharing knowledge between industry sites yields large benefits, especially in
the area of condition monitoring, it is often prohibited due to data privacy
issues. To tackle this situation, we propose an Autoencoder-based Federated
Learning method utilizing vibration sensor data from rotating machines, that
allows for a distributed training on edge devices, located on-premise and close
to the monitored machines. Preserving data privacy and at the same time
exonerating possibly unreliable network connections of remote sites, our
approach enables knowledge transfer across organizational boundaries, without
sharing the monitored data. We conducted an evaluation utilizing two real-world
datasets as well as multiple testbeds and the results indicate that our method
enables a competitive performance compared to previous results, while
significantly reducing the resource and network utilization.
