  Nowadays wireless communication is rapidly reshaping entire industry sectors.
In particular, mobile edge computing (MEC) as an enabling technology for
industrial Internet of things (IIoT) brings powerful computing/storage
infrastructure closer to the mobile terminals and, thereby, significant lowers
the response latency. To reap the benefit of proactive caching at the network
edge, precise knowledge on the popularity pattern among the end devices is
essential. However, the complex and dynamic nature of the content popularity
over space and time as well as the data-privacy requirements in many IIoT
scenarios pose tough challenges to its acquisition. In this article, we propose
an unsupervised and privacy-preserving popularity prediction framework for
MEC-enabled IIoT. The concepts of local and global popularities are introduced
and the time-varying popularity of each user is modelled as a model-free Markov
chain. On this basis, a novel unsupervised recurrent federated learning (URFL)
algorithm is proposed to predict the distributed popularity while achieve
privacy preservation and unsupervised training. Simulations indicate that the
proposed framework can enhance the prediction accuracy in terms of a reduced
root-mean-squared error by up to $60.5\%-68.7\%$. Additionally, manual labeling
and violation of users' data privacy are both avoided.
