Personalized Federated Learning over non-IID Data for Indoor Localization
This work addresses privacy and data heterogeneity challenges in indoor localization for users, but it is incremental as it builds on existing federated learning methods.
The paper tackles the problem of training accurate indoor localization models with non-IID data while preserving user privacy, by proposing a personalized federated learning approach that fuses models using Bayesian rules, achieving improved performance in simulations.
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.