There is a significant demand for indoor localization technology in smart buildings, and the most promising solution in this field is using RF sensors and fingerprinting-based methods that employ machine learning models trained on crowd-sourced user data gathered from IoT devices. However, this raises security and privacy issues in practice. Some researchers propose to use federated learning to partially overcome privacy problems, but there still remain security concerns, e.g., single-point failure and malicious attacks. In this paper, we propose a framework named DFLoc to achieve precise 3D localization tasks while considering the following two security concerns. Particularly, we design a specialized blockchain to decentralize the framework by distributing the tasks such as model distribution and aggregation which are handled by a central server to all clients in most previous works, to address the issue of the single-point failure for a reliable and accurate indoor localization system. Moreover, we introduce an updated model verification mechanism within the blockchain to alleviate the concern of malicious node attacks. Experimental results substantiate the framework's capacity to deliver accurate 3D location predictions and its superior resistance to the impacts of single-point failure and malicious attacks when compared to conventional centralized federated learning systems.

通过使用RF传感器和基于指纹的机器学习模型，结合从物联网设备中收集的众包用户数据训练，来实现室内定位技术，但这引发了实际中的安全和隐私问题。为了解决这些问题，本文提出了一种名为DFLoc的框架，通过设计一个专用的区块链来分散任务，解决可靠而准确的室内定位系统中的单点故障问题，并引入了一个更新的模型验证机制来缓解恶意节点攻击的关切。实验结果表明，该框架能够提供准确的三维定位预测，并在面对单点故障和恶意攻击时相比传统的集中式联邦学习系统具有更强的抵抗力。

一种可信的基于联邦学习和区块链的AIoT定位系统