Transferable Deployment of Semantic Edge Inference Systems via Unsupervised Domain Adaption
Journal:
arXiv
Published Date:
Apr 16, 2025
Abstract
This paper investigates deploying semantic edge inference systems for
performing a common image clarification task. In particular, each system
consists of multiple Internet of Things (IoT) devices that first locally encode
the sensing data into semantic features and then transmit them to an edge
server for subsequent data fusion and task inference. The inference accuracy is
determined by efficient training of the feature encoder/decoder using labeled
data samples. Due to the difference in sensing data and communication channel
distributions, deploying the system in a new environment may induce high costs
in annotating data labels and re-training the encoder/decoder models. To
achieve cost-effective transferable system deployment, we propose an efficient
Domain Adaptation method for Semantic Edge INference systems (DASEIN) that can
maintain high inference accuracy in a new environment without the need for
labeled samples. Specifically, DASEIN exploits the task-relevant data
correlation between different deployment scenarios by leveraging the techniques
of unsupervised domain adaptation and knowledge distillation. It devises an
efficient two-step adaptation procedure that sequentially aligns the data
distributions and adapts to the channel variations. Numerical results show
that, under a substantial change in sensing data distributions, the proposed
DASEIN outperforms the best-performing benchmark method by 7.09% and 21.33% in
inference accuracy when the new environment has similar or 25 dB lower channel
signal to noise power ratios (SNRs), respectively. This verifies the
effectiveness of the proposed method in adapting both data and channel
distributions in practical transfer deployment applications.
