  In manufacturing settings, data collection and analysis are often a
time-consuming, challenging, and costly process. It also hinders the use of
advanced machine learning and data-driven methods which require a substantial
amount of offline training data to generate good results. It is particularly
challenging for small manufacturers who do not share the resources of a large
enterprise. Recently, with the introduction of the Internet of Things (IoT),
data can be collected in an integrated manner across the factory in real-time,
sent to the cloud for advanced analysis, and used to update the machine
learning model sequentially. Nevertheless, small manufacturers face two
obstacles in reaping the benefits of IoT: they may be unable to afford or
generate enough data to operate a private cloud, and they may be hesitant to
share their raw data with a public cloud. Federated learning (FL) is an
emerging concept of collaborative learning that can help small-scale industries
address these issues and learn from each other without sacrificing their
privacy. It can bring together diverse and geographically dispersed
manufacturers under the same analytics umbrella to create a win-win situation.
However, the widespread adoption of FL across multiple manufacturing
organizations remains a significant challenge. This study aims to review the
challenges and future directions of applying federated learning in the
manufacturing industry, with a specific emphasis on the perspectives of
Industry 4.0 and 5.0.
