Enabling Edge Devices that Learn from Each Other: Cross Modal Training for Activity Recognition
Enabling Edge Devices that Learn from Each Other: Cross Modal Training for Activity Recognition
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DOI：
10.1145/3213344.3213351
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发表时间：
2018-06
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通讯作者：
Tianwei Xing;S. Sandha;Bharathan Balaji;Supriyo Chakraborty;M. Srivastava
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作者：
Tianwei Xing;S. Sandha;Bharathan Balaji;Supriyo Chakraborty;M. Srivastava
边缘设备在智能推理和模式匹配方面广泛依赖机器学习。然而，边缘设备使用多种传感模式，并处于广泛的环境中。由于人工标记无法扩展，很难为每种情况开发单独的机器学习模型。为了减少标记数据的数量并加快训练过程，我们建议通过使用未标记数据在边缘设备之间传递知识。我们的方法称为RecycleML，它使用跨模态迁移来加速不同传感模式下边缘设备的学习。以人类活动识别作为案例研究，在我们收集的CMActivity数据集上，我们观察到与从头开始训练边缘设备相比，RecycleML将所需标记数据的数量至少减少了90%，并将训练过程加快了多达50倍。
Edge devices rely extensively on machine learning for intelligent inferences and pattern matching. However, edge devices use a multitude of sensing modalities and are exposed to wide ranging contexts. It is difficult to develop separate machine learning models for each scenario as manual labeling is not scalable. To reduce the amount of labeled data and to speed up the training process, we propose to transfer knowledge between edge devices by using unlabeled data. Our approach, called RecycleML, uses cross modal transfer to accelerate the learning of edge devices across different sensing modalities. Using human activity recognition as a case study, over our collected CMActivity dataset, we observe that RecycleML reduces the amount of required labeled data by at least 90% and speeds up the training process by up to 50 times in comparison to training the edge device from scratch.