Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method

Abstract

Received Signal Strength (RSS) fingerprint-based localization has attracted a lot of research effort and cultivated many commercial applications of location-based services due to its low cost and ease of implementation. Many studies are exploring the use of deep learning (DL) algorithms for localization. DL's ability to extract features and to classify autonomously makes it an attractive solution for fingerprint-based localization. These solutions require frequent retraining of DL models with vast amounts of measurements. Although crowdsourcing is an excellent way to gather immense amounts of data, it jeopardizes the privacy of participants, as it requires to collect labeled data at a centralized server. Recently, federated learning has emerged as a practical concept in solving the privacy preservation issue of crowdsourcing participants by performing model training at the edge devices in a decentralized manner; the participants do not expose their data anymore to a centralized server. This paper presents a novel method utilizing federated learning to improve the accuracy of RSS fingerprint-based localization while preserving the privacy of the crowdsourcing participants. Employing federated learning allows ensuring \emph{preserving the privacy of user data} while enabling an adequate localization performance with experimental data captured in real-world settings. The proposed method improved localization accuracy by 1.8 meters when used as a booster for centralized learning and achieved satisfactory localization accuracy when used standalone.