Personalized Location-Preference Learning for Federated Task Assignment in Spatial Crowdsourcing

Abstract

With the proliferation of wireless and mobile devices, Spatial Crowdsourcing (SC) attracts increasing attention, where task assignment plays a critically important role. However, recent task assignment solutions in SC often assume that data is stored in a central station while ignoring the issue of privacy leakage. To enable decentralized training and privacy protection, we propose a federated task assignment framework with personalized location-preference learning, which performs efficient task assignment while keeping the data decentralized and private in each platform center (e.g., a delivery center of an SC company). The framework consists of two phases: personalized federated location-preference learning and task assignment. Specifically, in the first phase, we design a personalized location-preference learning model for each platform center by simultaneously considering the location information and data heterogeneity across platform centers. Based on workers' location preference, the task assignment phase aims to achieve effective and efficient task assignment by means of the Kuhn-Munkres (KM) algorithm and the newly proposed conditional degree-reduction algorithm. Extensive experiments on real-world data show the effectiveness of the proposed framework.