  Billions of IoT devices will be deployed in the near future, taking advantage
of faster Internet speed and the possibility of orders of magnitude more
endpoints brought by 5G/6G. With the growth of IoT devices, vast quantities of
data that may contain users' private information will be generated. The high
communication and storage costs, mixed with privacy concerns, will increasingly
challenge the traditional ecosystem of centralized over-the-cloud learning and
processing for IoT platforms. Federated Learning (FL) has emerged as the most
promising alternative approach to this problem. In FL, training data-driven
machine learning models is an act of collaboration between multiple clients
without requiring the data to be brought to a central point, hence alleviating
communication and storage costs and providing a great degree of user-level
privacy. However, there are still some challenges existing in the real FL
system implementation on IoT networks. In this paper, we will discuss the
opportunities and challenges of FL in IoT platforms, as well as how it can
enable diverse IoT applications. In particular, we identify and discuss seven
critical challenges of FL in IoT platforms and highlight some recent promising
approaches towards addressing them.
