> For the complete documentation index, see [llms.txt](https://neutrinoliu.gitbook.io/fiesta/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://neutrinoliu.gitbook.io/fiesta/welcome.md).

# Welcome

**Spectrum sensing in large temporal-spacial area** can be widely applied to a variety of research objectives, including spectrum estimation and anomaly detection. A naive idea is everyone contributing his sampled data and sharing it with the community. However, **Privacy** and **Incentive** become two challenges we have to face before the big map of crowdsource could be achieved. 

In Fiesta project, we leverage **Federated Learning** and **Blockchain** techniques to solve this problem, stimulating participants to contribute their spectrum sensing data using fair rewards, with their privacy retained in the meantime.

> **This project has been accepted in DySPAN2024 🤗**
>
> Yijing Zeng, Bangya Liu, Yilong Li, Domenico Giustiniano, and Suman Banerjee. "Sustainable Spectrum Crowdsensing." IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). 2024.

### How does the project work

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[Project Composition](/fiesta/project-composition.md)
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### Join in Fiesta project in seconds

{% content-ref url="/pages/GitgVo3rPkF1GTGsJM9E" %}
[Quick Start](/fiesta/quick-start.md)
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### Two demo applications

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[Federated Learning (TBD)](/fiesta/federated-learning-tbd.md)
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### Developer references

{% content-ref url="/pages/xBW6w9bJM1EgbCHPbTOO" %}
[BlockChain (TBD)](/fiesta/blockchain-tbd.md)
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[API Reference](/fiesta/api-reference.md)
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