  Internet of Things (IoT) interconnects a massive amount of devices,
generating heterogeneous data with diverse characteristics. IoT data emerges as
a vital asset for data-intensive IoT applications, such as healthcare, smart
city and predictive maintenance, harnessing the vast volume of heterogeneous
data to its maximum advantage. These applications leverage different Artificial
Intelligence (AI) algorithms to discover new insights. While machine learning
effectively uncovers implicit patterns through model training, centralizing IoT
data for training poses significant privacy and security concerns. Federated
Learning (FL) offers an promising solution, allowing IoT devices to conduct
local learning without sharing raw data with third parties. Model-heterogeneous
FL empowers clients to train models with varying complexities based on their
hardware capabilities, aligning with heterogeneity of devices in real-world IoT
environments. In this article, we review the state-of-the-art
model-heterogeneous FL methods and provide insights into their merits and
limitations. Moreover, we showcase their applicability to IoT and identify the
open problems and future directions. To the best of our knowledge, this is the
first article that focuses on the topic of model-heterogeneous FL for IoT.
