Task Assignment with Federated Preference Learning in Spatial Crowdsourcing
Jiaxin Liu, Liwei Deng, Miao Hao, Yan Zhao, Kai Zheng
Abstract
Spatial Crowdsourcing (SC) is ubiquitous in the online world today. As we have transitioned from crowdsourcing applications (e.g., Wikipedia) to SC applications (e.g., Uber), there is a substantial precedent that SC systems have a responsibility not only to effective task assignment but also to privacy protection. To address these often-conflicting responsibilities, we propose a framework, Task Assignment with Federated Preference Learning, which performs task assignment based on worker preferences while keeping the data decentralized and private in each platform center (e.g., each delivery center of an SC company). The framework includes two phases, i.e., a federated preference learning and a task assignment phase. Specifically, in the first phase, we design a local preference model for each platform center based on historical data. Meanwhile, the horizontal federated learning with a client-server structure is introduced to collaboratively train these local preference models under the orchestration of a central server. The task assignment phase aims to achieve effective and efficient task assignment by considering workers' preferences. Extensive evaluations over real data show the effectiveness and efficiency of the paper's proposals.