TY - JOUR
T1 - An Efficient Edge-Cloud Partitioning of Random Forests for Distributed Sensor Networks
AU - Shen, Tianyi
AU - Mishra, Cyan Subhra
AU - Sampson, Jack
AU - Kandemir, Mahmut Taylan
AU - Narayanan, Vijaykrishnan
N1 - Publisher Copyright:
© 2009-2012 IEEE.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - Intelligent edge sensors that augment legacy 'unintelligent' manufacturing systems provides cost-effective functional upgrades. However, the limited computing at these edge devices requires tradeoffs in efficient edge-cloud partitioning and raises data privacy issues. This work explores policies for partitioning random forest approaches, which are widely used for inference tasks in smart manufacturing, among sets of devices with different resources and data visibility. We demonstrate, using both publicly available datasets and a real-world grinding machine deployment, that our privacy-preserving approach to partitioning and training offers superior latency-accuracy tradeoffs to purely on-edge computation while still achieving much of the benefits from data-sharing cloud offload strategies.
AB - Intelligent edge sensors that augment legacy 'unintelligent' manufacturing systems provides cost-effective functional upgrades. However, the limited computing at these edge devices requires tradeoffs in efficient edge-cloud partitioning and raises data privacy issues. This work explores policies for partitioning random forest approaches, which are widely used for inference tasks in smart manufacturing, among sets of devices with different resources and data visibility. We demonstrate, using both publicly available datasets and a real-world grinding machine deployment, that our privacy-preserving approach to partitioning and training offers superior latency-accuracy tradeoffs to purely on-edge computation while still achieving much of the benefits from data-sharing cloud offload strategies.
UR - http://www.scopus.com/inward/record.url?scp=85141459630&partnerID=8YFLogxK
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U2 - 10.1109/LES.2022.3207968
DO - 10.1109/LES.2022.3207968
M3 - Article
AN - SCOPUS:85141459630
SN - 1943-0663
VL - 16
SP - 21
EP - 24
JO - IEEE Embedded Systems Letters
JF - IEEE Embedded Systems Letters
IS - 1
ER -