  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.
