@inproceedings{745f316f88284f84b7562a755fd79f58,
title = "Wireless Ad-Hoc Federated Learning for Cooperative Map Creation and Localization Models",
abstract = "Although Wi-Fi signals have been used for localization, many existing methods require gathering Wi-Fi information about the area in advance. This study proposed a novel system in which wireless ad-hoc federated learning is used to learn localization models and create maps cooperatively during regular movement. In this system, a combination of classification models is used to perform localization from Wi-Fi signal strength measured as received signal strength indicator (RSSI). In this study, RSSI data in a real-world Wi-Fi environment were collected to train and test localization models. The proposed method achieved localization accuracy between 91.30\% and 96.11 \%, which demonstrated the ability of the method to train localization models collaboratively.",
keywords = "Ad-Hoc Federated Learning, Collaborating Learning, Localization, RSSI",
author = "Yusuke Sugizaki and Hideya Ochiai and Muhammad Asad and Manabu Tsukada and Hiroshi Esaki",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th IEEE World Forum on Internet of Things, WF-IoT 2023 ; Conference date: 12-10-2023 Through 27-10-2023",
year = "2023",
doi = "10.1109/WF-IoT58464.2023.10539517",
language = "English",
series = "2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE World Forum on Internet of Things",
address = "United States",
}