2022 Fiscal Year Annual Research Report
Heterogeneous Graph Neural Network based Federated Mobile Crowdsensing
| Project/Area Number |
22H03573
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| Allocation Type | Single-year Grants |
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| Research Institution | The University of Tokyo |
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Principal Investigator |
FAN ZIPEI 東京大学, 空間情報科学研究センター, 特任講師 (70835397)
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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Keywords | Crowdsensing / Graph Neural Network / Privacy Preserving / Data Valuation |
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| Outline of Annual Research Achievements |
This year, I initiated the research on heterogeneous graph neural network based privacy preserving crowdsensing system. In this year, I have achieved several publications: one paper on using heterogeneous graph neural network for location-based services and published in CIKM 2022 and WWWJ 2022, and two papers on federated learning based crowdsensing system are published in IOTJ 2022, heterogeneous graph neural network based methods for crowdsensing way of indoor localization is published in IOTJ 2023, and shapley-based data valuation method for traffic prediction in Sensors 2022.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
This year, I have moved smoothly on the developing the heterogeneous graph neural network privacy preserving crowdsensing system. Although the complete system is not finished yet, but most of the core components have been researched and prototypes have been developed.
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| Strategy for Future Research Activity |
In the second year of this project, I planned to further conduct relevant research on these technics as planned, and also try to have a better integration of these technics to model a crowdsensing system that is both practical, privacy preserving and fair.
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