Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatforms

• URL: http://arxiv.org/abs/2101.01266v1
• Date: Mon, 4 Jan 2021 22:43:24 GMT
• Title: Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatforms
• Authors: Zhiyan Chen, Murat Simsek, Burak Kantarci
• Abstract summary: Mobile crowdsensing (MCS) leverages distributed and non-dedicated sensing concepts by utilizing sensors in a large number of mobile smart devices. A malicious user submitting fake sensing tasks to an MCS platform may be attempting to consume resources from any number of participants' devices. A novel approach is proposed to identify fake tasks that contain a number of independent detection devices and an aggregation entity.
• Score: 9.925311092487851
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Mobile crowdsensing (MCS) leverages distributed and non-dedicated sensing concepts by utilizing sensors imbedded in a large number of mobile smart devices. However, the openness and distributed nature of MCS leads to various vulnerabilities and consequent challenges to address. A malicious user submitting fake sensing tasks to an MCS platform may be attempting to consume resources from any number of participants' devices; as well as attempting to clog the MCS server. In this paper, a novel approach that is based on horizontal federated learning is proposed to identify fake tasks that contain a number of independent detection devices and an aggregation entity. Detection devices are deployed to operate in parallel with each device equipped with a machine learning (ML) module, and an associated training dataset. Furthermore, the aggregation module collects the prediction results from individual devices and determines the final decision with the objective of minimizing the prediction loss. Loss measurement considers the lost task values with respect to misclassification, where the final decision utilizes a risk-aware approach where the risk is formulated as a function of the utility loss. Experimental results demonstrate that using federated learning-driven illegitimate task detection with a risk aware aggregation function improves the detection performance of the traditional centralized framework. Furthermore, the higher performance of detection and lower loss of utility can be achieved by the proposed framework. This scheme can even achieve 100%detection accuracy using small training datasets distributed across devices, while achieving slightly over an 8% increase in detection improvement over traditional approaches.

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