用联邦学习实现隐私保护的室内定位，效果接近中心化模型。
A Privacy-Preserving Indoor Localization System based on Hierarchical Federated Learning
- 基于分层联邦学习与深度神经网络，避免数据集中收集。
- 实验显示定位性能接近中心化模型，且节省带宽、提升可靠性。
- 适合注重隐私与系统稳定的物联网定位场景。
位置信息是众多物联网应用的基础。传统室内定位技术常因集中式数据收集导致显著误差和隐私问题。机器学习虽能捕捉室内环境变化，但通常需集中聚合数据，引发隐私、带宽和服务器可靠性问题。本文提出一种基于联邦学习（FL）的动态室内定位方法，采用深度神经网络（DNN）模型。实验表明，该方法在保持数据隐私、带宽效率和服务器可靠性的同时，性能接近中心化模型（CL）。研究证明，所提联邦学习方案为增强隐私的室内定位提供了可行路径，推动安全高效的室内定位系统发展。
原文摘要 · Abstract (English)
Location information serves as the fundamental element for numerous Internet of Things (IoT) applications. Traditional indoor localization techniques often produce significant errors and raise privacy concerns due to centralized data collection. In response, Machine Learning (ML) techniques offer promising solutions by capturing indoor environment variations. However, they typically require central data aggregation, leading to privacy, bandwidth, and server reliability issues. To overcome these challenges, in this paper, we propose a Federated Learning (FL)-based approach for dynamic indoor localization using a Deep Neural Network (DNN) model. Experimental results show that FL has the nearby performance to Centralized Model (CL) while keeping the data privacy, bandwidth efficiency and server reliability. This research demonstrates that our proposed FL approach provides a viable solution for privacy-enhanced indoor localization, paving the way for advancements in secure and efficient indoor localization systems.
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