| 研究課題/領域番号 |
22H03573
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| 配分区分 | 補助金 |
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| 研究機関 | 東京大学 |
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研究代表者 |
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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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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| キーワード | Crowdsensing / Graph Neural Network / Privacy Preserving / Data Valuation |
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| 研究実績の概要 |
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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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
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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| 今後の研究の推進方策 |
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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