Paper Info
Title
Communication-Oriented Model Fine-Tuning for Packet-Loss Resilient Distributed Inference Under Highly Lossy IoT Networks
Abstract
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.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3149336
IEEE ACCESS
Keywords
DocType
Volume
Servers, Packet loss, Training, Robustness, Protocols, Internet of Things, Task analysis, Distributed inference, communication-efficiency, machine learning, packet loss tolerant, delay-aware system
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Sohei Itahara102.03
Takayuki Nishio210638.21
Yusuke Koda301.35
Koji Yamamoto413545.58