  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.
