Abstract
With the expansion of intelligent services and applications powered by Artificial Intelligence (AI), the Internet of Things (IoT) permeates many aspects of our everyday lives. In order to enable several intelligent IoT applications (i.e., indoor and outdoor), Federated Learning (FL) allows AI training at remote smart devices without the requirement for data sharing. First, with a discussion of the recent developments in FL and indoor smart device networks and their integration, we present a thorough assessment of the growing applications of FL in indoor networks. Next, we investigate and evaluate FL’s potential for allowing various indoor applications and services, such as data sharing, localization, navigation, human activity recognition, and security and privacy. FL in numerous critical indoor services and applications, including smart manufacturing, smart homes, and smart healthcare, is then thoroughly discussed. Finally, we conclude this survey by outlining the challenges and potential paths for further study in this rapidly expanding field.
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