Federated Learning is a technique of machine Learning that aims in preserving the privacy of user data. While in this process it enables the training of a Machine Learning (ML) model. Today I will discuss the applications of Federated Learning in IoT Applications.
Since the sensor devices in IoT applications generate a massive amount of user data, it is important to preserve the privacy of user data. Hence, it is relevant to consider Federated Learning as the solution. Because it is inherently distributed in nature and allows the participant devices to collaborate in building a machine learning model. Also, there are a number of ways in which the Federated Learning scheme can maintain the privacy and security of user data.
In contrast to the traditional distributed scenarios where the data is available in Independent and Identically distributed formats (IID), IoT applications often have their data in non-IID formats. This happens because often the devices that make an IoT application are heterogeneous in nature and collect unequal volumes of data. Therefore, the data that these devices use for training is variable.
IoT Applications where Federated Learning can be Applied
Since IoT has been used extensively in Intelligent Transportation systems, there are frequent data updates. Therefore, creating a new learning model every time is not possible with the cloud server. However, we can update the local model at the specific IoT devices and further send this local model to the aggregation server.
Similarly, in the case of smart manufacturing, each machine is equipped with its own set of sensors that also constantly collect data from the environment. In such a case, it is preferable to update the local model at specific machines.
Also, in smart healthcare, if the clinics are monitoring the patients remotely using IoT-enabled applications, the data collected by the wearable devices should remain at the local devices. Hence this application is also a perfect candidate for Federated Learning.
This article discusses how Federated Learning provides solutions to many types of IoT applications. Because of its distributed nature, Federated Learning can be used in IoT applications. Since these applications also perform computation and model building in a distributed way by involving individual sensor nodes. However, there are certain challenges also that need to be addressed. Only after that, we can exploit the full potential of Federated Learning in IoT applications.