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The distributed inference (DI) framework has gained traction as a techniquefor real-time applications empowered by cutting-edge deep machine learning (ML)on resource-constrained Internet of things (IoT) devices. In DI, computationaltasks are offloaded from the IoT device to the edge server via lossy IoTnetworks. However, generally, there is a communication system-level trade-offbetween communication latency and reliability; thus, to provide accurate DIresults, a reliable and high-latency communication system is required to beadapted, which results in non-negligible end-to-end latency of the DI. Thismotivated us to improve the trade-off between the communication latency andaccuracy by efforts on ML techniques. Specifically, we have proposed acommunication-oriented model tuning (COMtune), which aims to achieve highlyaccurate 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 unreliablecommunication links through the application of the dropout technique. Thisenables the DI system to obtain robustness against unreliable communicationlinks. Our ML experiments revealed that COMtune enables accurate predictionswith low latency and under lossy networks.

Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks