  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.
