为泰国朱拉隆功大学构建专用建筑识别数据集，支持边缘协同学习。
University Building Recognition Dataset in Thailand for the mission-oriented IoT sensor system
- 基于视觉变换器的无线自组网联邦学习框架，实现设备端协作训练。
- 在自建数据集上，联邦学习精度优于自训练方法（89.2% vs 83.1%）。
- 适合物联网场景下本地化、隐私保护的智能建筑识别应用。
多个工业领域已在边缘设备上部署机器学习推理。未来趋势显示，随着半导体性能提升，边缘设备上的模型训练也具有潜力。无线自组网联邦学习（WAFL）通过设备间直接通信，成为协同学习的可行方案。特别是采用视觉变换器的WAFL（WAFL-ViT）已在东京大学建筑识别数据集（UTBR）上验证了图像识别能力。由于WAFL是面向特定任务的传感器系统，需为每项任务构建专用数据集。本文以泰国朱拉隆功大学为例，构建了专用的朱拉隆功大学建筑识别数据集（CUBR）。实验结果表明，在联邦学习场景下训练的模型精度（89.2%）显著高于自训练场景（83.1%）。数据集已开源：https://github.com/jo2lxq/wafl/。
原文摘要 · Abstract (English)
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/.
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