  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.
