Personalized Federated Learning over non-IID Data for Indoor Localization

• URL: http://arxiv.org/abs/2107.04189v1
• Date: Fri, 9 Jul 2021 03:31:16 GMT
• Title: Personalized Federated Learning over non-IID Data for Indoor Localization
• Authors: Peng Wu, Tales Imbiriba, Junha Park, Sunwoo Kim, Pau Closas
• Abstract summary: We consider the use of recent Federated Learning schemes to train a set of personalized models. 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.
• Score: 17.03722514121803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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