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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.05468 |
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| _version_ | 1866908694867869696 |
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| author | Taniguchi, Takara Ueda, Yudai Muramatsu, Atsuya Hashimoto, Kohki Yagi, Ryo Ochiai, Hideya Aswakul, Chaodit |
| author_facet | Taniguchi, Takara Ueda, Yudai Muramatsu, Atsuya Hashimoto, Kohki Yagi, Ryo Ochiai, Hideya Aswakul, Chaodit |
| contents | Many industrial sectors have been using of machine learning at inference mode on edge devices. Future directions show that training on edge devices is promising due to improvements in semiconductor performance. Wireless Ad Hoc Federated Learning (WAFL) has been proposed as a promising approach for collaborative learning with device-to-device communication among edges. In particular, WAFL with Vision Transformer (WAFL-ViT) has been tested on image recognition tasks with the UTokyo Building Recognition Dataset (UTBR). Since WAFL-ViT is a mission-oriented sensor system, it is essential to construct specific datasets by each mission. In our work, we have developed the Chulalongkorn University Building Recognition Dataset (CUBR), which is specialized for Chulalongkorn University as a case study in Thailand. Additionally, our results also demonstrate that training on WAFL scenarios achieves better accuracy than self-training scenarios. Dataset is available in https://github.com/jo2lxq/wafl/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05468 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | University Building Recognition Dataset in Thailand for the mission-oriented IoT sensor system Taniguchi, Takara Ueda, Yudai Muramatsu, Atsuya Hashimoto, Kohki Yagi, Ryo Ochiai, Hideya Aswakul, Chaodit Computer Vision and Pattern Recognition Artificial Intelligence Many industrial sectors have been using of machine learning at inference mode on edge devices. Future directions show that training on edge devices is promising due to improvements in semiconductor performance. Wireless Ad Hoc Federated Learning (WAFL) has been proposed as a promising approach for collaborative learning with device-to-device communication among edges. In particular, WAFL with Vision Transformer (WAFL-ViT) has been tested on image recognition tasks with the UTokyo Building Recognition Dataset (UTBR). Since WAFL-ViT is a mission-oriented sensor system, it is essential to construct specific datasets by each mission. In our work, we have developed the Chulalongkorn University Building Recognition Dataset (CUBR), which is specialized for Chulalongkorn University as a case study in Thailand. Additionally, our results also demonstrate that training on WAFL scenarios achieves better accuracy than self-training scenarios. Dataset is available in https://github.com/jo2lxq/wafl/. |
| title | University Building Recognition Dataset in Thailand for the mission-oriented IoT sensor system |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2512.05468 |