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.
Original language | English |
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Title of host publication | Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23) |
Number of pages | 10 |
Publisher | Association for Computing Machinery |
Publication date | 21 Oct 2023 |
Pages | 3534-3543 |
ISBN (Print) | 979-8-4007-0124-5 |
DOIs | |
Publication status | Published - 21 Oct 2023 |
Event | 32nd ACM International Conference on Information and Knowledge Management - Birmingham, United Kingdom Duration: 21 Oct 2023 → 25 Oct 2023 |
Conference
Conference | 32nd ACM International Conference on Information and Knowledge Management |
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Country/Territory | United Kingdom |
City | Birmingham |
Period | 21/10/2023 → 25/10/2023 |
Keywords
- federated learning
- preference
- spatial crowdsourcing
- task assignment