Personalized Federated Learning over non-IID Data for Indoor Localization
Personalized Federated Learning over non-IID Data for Indoor Localization
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DOI：
10.1109/spawc51858.2021.9593115
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Peng Wu;T. Imbiriba;Junha Park-;Sunwoo Kim;P. Closas
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文献类型：
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作者：
Peng Wu;T. Imbiriba;Junha Park-;Sunwoo Kim;P. Closas
由于无线信道传播模型的物理特性的复杂性，使用数据驱动的方法定位和跟踪对象是一个热门话题。在这些建模方法中，需要收集数据以准确地训练模型，同时维护用户的隐私。一个有吸引力的计划，合作实现这些目标被称为联邦学习（FL）。FL方案中的一个挑战是存在非独立和同分布（非IID）数据，这是由不同区域的不均匀探索造成的。在本文中，我们考虑使用最近FL计划来训练一组个性化模型，然后通过贝叶斯规则进行最佳融合，这使得它适合在室内定位的背景下。
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.