TY - GEN
T1 - Personalized Federated Learning over non-IID Data for Indoor Localization
AU - Wu, Peng
AU - Imbiriba, Tales
AU - Park, Junha
AU - Kim, Sunwoo
AU - Closas, Pau
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Localization and tracking of objects using data-driven methods is a popular topic due to the complexity in characterizing the physics of wireless channel propagation models. In these modeling approaches, data needs to be gathered to accurately train models, at the same time that user's privacy is maintained. An appealing scheme to cooperatively achieve these goals is known as Federated Learning (FL). A challenge in FL schemes is the presence of non-independent and identically distributed (non-IID) data, caused by unevenly exploration of different areas. In this paper, we consider the use of recent FL schemes to train a set of personalized models that are then optimally fused through Bayesian rules, which makes it appropriate in the context of indoor localization.
AB - Localization and tracking of objects using data-driven methods is a popular topic due to the complexity in characterizing the physics of wireless channel propagation models. In these modeling approaches, data needs to be gathered to accurately train models, at the same time that user's privacy is maintained. An appealing scheme to cooperatively achieve these goals is known as Federated Learning (FL). A challenge in FL schemes is the presence of non-independent and identically distributed (non-IID) data, caused by unevenly exploration of different areas. In this paper, we consider the use of recent FL schemes to train a set of personalized models that are then optimally fused through Bayesian rules, which makes it appropriate in the context of indoor localization.
KW - Bayesian inference
KW - data-driven
KW - Federated Learning
KW - localization
KW - non-IID
UR - https://www.scopus.com/pages/publications/85122830749
UR - https://www.scopus.com/pages/publications/85122830749#tab=citedBy
U2 - 10.1109/SPAWC51858.2021.9593115
DO - 10.1109/SPAWC51858.2021.9593115
M3 - Conference contribution
AN - SCOPUS:85122830749
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
SP - 421
EP - 425
BT - 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021
Y2 - 27 September 2021 through 30 September 2021
ER -