Towards Proactive Context-Aware IoT Environments by Means of Federated Learning

Internet of Things (IoT) integrates billions of smart devices and keeps growing. IoT technologies play a crucial role in smart applications that improve the quality of life. Likewise, the computational capacity of mobile devices has greatly increased, opening up new possibilities. In many cases, human interaction is necessary for IoT devices to perform properly. Users must configure more and more devices, investing time and effort. Artificial Intelligence (AI) techniques are currently used to predict user needs and behavior, trying to adapt devices to user preferences. However, achieving all-purpose models is a challenging task, aggravated by long training periods preventing personalized models in the early stages. This paper proposes a solution based on Federated Learning to predict behaviors in different environments and improve user’s coexistence with IoT devices, avoiding most manual interactions and making use of mobile devices capabilities. Federation allows new users’ predictions to be done using other users’ previous behaviors in similar environments. Also, it provides closer customization, immediate availability and avoids most manual device interactions.