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

• URL: http://arxiv.org/abs/2407.07921v1
• Date: Mon, 8 Jul 2024 04:14:19 GMT
• Title: A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain
• Authors: Junfei Wang, He Huang, Jingze Feng, Steven Wong, Lihua Xie, Jianfei Yang,
• Abstract summary: We propose a framework named DFLoc to achieve precise 3D localization tasks. Specifically, we address the issue of single-point failure for a reliable and accurate indoor localization system. We introduce an updated model verification mechanism within the blockchain to alleviate the concern of malicious node attacks.
• Score: 29.968086297894626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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