A Trustworthy AIoT-Enabled Localization System via Federated Learning and Blockchain

There is a significant demand for indoor localization technology in smart buildings, and the most promising solution in this field is using radio frequency (RF) sensors and fingerprinting-based methods that employ machine learning models trained on crowd-sourced user data gathered from Internet of Things (IoT) devices. However, this raises security and privacy issues in practice. Some researchers propose to use federated learning (FL) to partially overcome privacy problems, but there still remain security concerns, e.g., single-point failure and malicious attacks. In this article, we propose a framework named DFLoc to achieve precise 3-D 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 tackle the single-point failure issue in ensuring reliable and accurate indoor localization. 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 3-D location predictions and its superior resistance to the impacts of single-point failure and malicious attacks when compared to conventional centralized FL systems.