Journal
IEEE ACCESS
Volume 9, Issue -, Pages 29180-29199Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3059251
Keywords
Training; Data models; Image edge detection; Deep learning; Task analysis; Cloud computing; Tensors; Incremental learning; convolutional neural network; IoT edge device; cloud; data sampling
Categories
Funding
- University of Nottingham Malaysia Campus
- Fundamental Research Grant Scheme (FRGS) by the Ministry of Higher Education, Malaysia [FRGS/1/2018/ICT02/UNIM/02/4]
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With the exponential growth of IoT devices, the challenge lies in the transmission cost of data in the edge-cloud architecture. Our proposed methods effectively perform class-incremental learning.
With the exponential rise of the number of IoT devices, the amount of data being produced is massive. Thus, it is unfeasible to send all the raw data directly to the cloud for processing, especially for data that is high dimensional. Training deep learning models incrementally evolves the model over time and eliminates the need to statically training the models with all the data. However, the integration of class incremental learning and the Internet of Things (IoT) is a new concept and is not yet mature. In the context of IoT and deep learning, the transmission cost of data in the edge-cloud architecture is a challenge. We demonstrate a novel sample selection method that discards certain training images on the IoT edge device that reduces transmission cost and still maintains class incremental learning performance. It can be unfeasible to transmit all parameters of a trained model back to the IoT edge device. Therefore, we propose an algorithm to find only the useful parameters of a trained model in an efficient way to reduce the transmission cost from the cloud to the edge devices. Results show that our proposed methods can effectively perform class-incremental learning in an edge-cloud setting.
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