mlsanityData to 7 Oct 2026

University Building Recognition Dataset in Thailand for the mission-oriented IoT sensor system

arXiv 2512.05468 ↗First posted 5 Dec 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

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/.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 5 Dec 2025.

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References · 10

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  9. 2022Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learningcited by 4
  10. 2022Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attackscited by 2

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