TY - JOUR
T1 - Pseudo Label-Driven Federated Learning-Based Decentralized Indoor Localization via Mobile Crowdsourcing
AU - Li, Wei
AU - Zhang, Cheng
AU - Tanaka, Yoshiaki
N1 - Funding Information:
Manuscript received March 12, 2020; revised May 19, 2020; accepted May 20, 2020. Date of publication May 28, 2020; date of current version September 3, 2020. The work of Wei Li was supported by the China Scholarship Council (CSC) under Grant 201706690030. The associate editor coordinating the review of this article and approving it for publication was Dr. Yen Kheng Tan. (Corresponding author: Wei Li.) Wei Li and Cheng Zhang are with the Department of Computer Science and Communications Engineering, Waseda University, Tokyo 169-8555, Japan (e-mail: liwei@akane.waseda.jp; cheng.zhang@akane.waseda.jp).
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - Received signal strength (RSS) fingerprint-based indoor localization has received increasing popularity over the past decades. However, it suffers from the high calibration effort for fingerprint collection. In this paper, a Centralized indooR localizatioN method using Pseudo-label (CRNP) is proposed, which employs a small set of labeled data (RSS fingerprint) along with large volumes of unlabeled data (RSS values without coordinates) to reduce the workload of labeled data collection and improve the indoor localization performance. However, the rich location data is large in quantity and privacy sensitive, which may lead to high network cost (i.e., data transmission cost, data storage cost) and potential privacy leakage for data transmission to the central server. Therefore, a decentralized indoor localization method incorporating CRNP and federated learning is devised, which keeps the location data on local users' devices and improves the shared CRNP model by aggregating users' updates of the model. The experiment results demonstrate that (i) the proposed CRNP enables to improve the indoor localization accuracy by using unlabeled crowdsourced data; (ii) the designed decentralized scheme is robust to different data distribution and is capable to reduce the network cost and prevent users' privacy leakage.
AB - Received signal strength (RSS) fingerprint-based indoor localization has received increasing popularity over the past decades. However, it suffers from the high calibration effort for fingerprint collection. In this paper, a Centralized indooR localizatioN method using Pseudo-label (CRNP) is proposed, which employs a small set of labeled data (RSS fingerprint) along with large volumes of unlabeled data (RSS values without coordinates) to reduce the workload of labeled data collection and improve the indoor localization performance. However, the rich location data is large in quantity and privacy sensitive, which may lead to high network cost (i.e., data transmission cost, data storage cost) and potential privacy leakage for data transmission to the central server. Therefore, a decentralized indoor localization method incorporating CRNP and federated learning is devised, which keeps the location data on local users' devices and improves the shared CRNP model by aggregating users' updates of the model. The experiment results demonstrate that (i) the proposed CRNP enables to improve the indoor localization accuracy by using unlabeled crowdsourced data; (ii) the designed decentralized scheme is robust to different data distribution and is capable to reduce the network cost and prevent users' privacy leakage.
KW - Indoor localization
KW - federated learning
KW - pseudo label
KW - stacked autoencoder
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U2 - 10.1109/JSEN.2020.2998116
DO - 10.1109/JSEN.2020.2998116
M3 - Article
AN - SCOPUS:85091047686
SN - 1530-437X
VL - 20
SP - 11556
EP - 11565
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 19
M1 - 9103044
ER -