Federal AI for Indoor Localization

Abstract

This paper examines the transformative role of Federal AI (FAI), particularly Federated Learning (FL), in advancing indoor localization systems. It addresses the increasing need for accurate and dependable positioning solutions while maintaining user privacy. The survey reviews various indoor localization technologies, analyzing their limitations and the specific challenges posed by indoor environments. It highlights the principles of FAI and its advantages, including user privacy preservation, scalability, and support for personalization. The paper also explores practical applications of FAI in areas such as personalized navigation, asset tracking, and other location-based services. It provides a detailed overview of existing FAI methodologies for indoor localization, discussing their key contributions. Finally, it identifies unresolved challenges and outlines potential research directions, emphasizing the promising future of FAI in this domain.