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.

翻译：众包是一种通过利用人类智能来处理计算机难以完成的任务的有利计算范式。然而，通用的众包系统可能因共享工作者数据而导致隐私泄露。为解决此问题，我们提出一种新方法，称为iFedCrowd（激励增强型联邦众包），用于管理众包项目的隐私与质量。iFedCrowd允许参与者本地处理敏感数据，仅上传加密的训练模型，然后聚合模型参数以构建共享服务器模型，从而保护数据隐私。为了有效激励工作者构建高质量的全局模型，我们引入一种激励机制，鼓励工作者持续收集新鲜数据以训练准确的客户端模型，并促进全局模型训练。我们将众包平台与参与工作者之间的激励性交互建模为Stackelberg博弈，其中每一方都最大化自身收益。我们推导出该博弈的纳什均衡，以找到双方的最优解。实验结果证实，iFedCrowd能够高效、高质量地完成安全众包项目。