  Crowdsourcing is a favorable computing paradigm for processing computer-hard
tasks by harnessing human intelligence. However, generic crowdsourcing systems
may lead to privacy-leakage through the sharing of worker data. To tackle this
problem, we propose a novel approach, called iFedCrowd (incentive-boosted
Federated Crowdsourcing), to manage the privacy and quality of crowdsourcing
projects. iFedCrowd allows participants to locally process sensitive data and
only upload encrypted training models, and then aggregates the model parameters
to build a shared server model to protect data privacy. To motivate workers to
build a high-quality global model in an efficacy way, we introduce an incentive
mechanism that encourages workers to constantly collect fresh data to train
accurate client models and boosts the global model training. We model the
incentive-based interaction between the crowdsourcing platform and
participating workers as a Stackelberg game, in which each side maximizes its
own profit. We derive the Nash Equilibrium of the game to find the optimal
solutions for the two sides. Experimental results confirm that iFedCrowd can
complete secure crowdsourcing projects with high quality and efficiency.
