  Industrial Internet of Things (IIoT) lays a new paradigm for the concept of
Industry 4.0 and paves an insight for new industrial era. Nowadays smart
machines and smart factories use machine learning/deep learning based models
for incurring intelligence. However, storing and communicating the data to the
cloud and end device leads to issues in preserving privacy. In order to address
this issue, federated learning (FL) technology is implemented in IIoT by the
researchers nowadays to provide safe, accurate, robust and unbiased models.
Integrating FL in IIoT ensures that no local sensitive data is exchanged, as
the distribution of learning models over the edge devices has become more
common with FL. Therefore, only the encrypted notifications and parameters are
communicated to the central server. In this paper, we provide a thorough
overview on integrating FL with IIoT in terms of privacy, resource and data
management. The survey starts by articulating IIoT characteristics and
fundamentals of distributive and FL. The motivation behind integrating IIoT and
FL for achieving data privacy preservation and on-device learning are
summarized. Then we discuss the potential of using machine learning, deep
learning and blockchain techniques for FL in secure IIoT. Further we analyze
and summarize the ways to handle the heterogeneous and huge data. Comprehensive
background on data and resource management are then presented, followed by
applications of IIoT with FL in healthcare and automobile industry. Finally, we
shed light on challenges, some possible solutions and potential directions for
future research.
