S. Itahara, T. Nishio, Y. Koda and K. Yamamoto, "Communication-Oriented Model Fine-Tuning for Packet-Loss Resilient Distributed Inference Under Highly Lossy IoT Networks," in IEEE Access, vol. 10, pp. 14969-14979, 2022, doi: 10.1109/ACCESS.2022.3149336.
Communication-oriented model fine-tuning for packet-loss resilient distributed inference under highly lossy IoT networks
|Author:||Itahara, Sohei1; Nishio, Takayuki2; Koda, Yusuke3;|
1Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
2School of Engineering, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
3Centre of Wireless Communications, University of Oulu, 90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 6.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022061647108
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-08-18
The distributed inference (DI) framework has gained traction as a technique for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In DI, computational tasks are offloaded from the IoT device to the edge server via lossy IoT networks. However, generally, there is a communication system-level trade-off between communication latency and reliability; thus, to provide accurate DI results, a reliable and high-latency communication system is required to be adapted, which results in non-negligible end-to-end latency of the DI. This motivated us to improve the trade-off between the communication latency and accuracy by efforts on ML techniques. Specifically, we have proposed a communication-oriented model tuning (COMtune), which aims to achieve highly accurate DI with low-latency but unreliable communication links. In COMtune, the key idea is to fine-tune the ML model by emulating the effect of unreliable communication links through the application of the dropout technique. This enables the DI system to obtain robustness against unreliable communication links. Our ML experiments revealed that COMtune enables accurate predictions with low latency and under lossy networks.
|Pages:||14969 - 14979|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by JST PRESTO under Grant JPMJPR2035.
© 2022 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/