Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification
<|reference_start|>Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification: Semantic and goal-oriented (SGO) communication is an emerging technology that only transmits significant information for a given task. Semantic communication encounters many challenges, such as computational complexity at end users, availability of data, and privacy-preserving. This work presents a TinyML-based semantic communication framework for few-shot wireless image classification that integrates split-learning and meta-learning. We exploit split-learning to limit the computations performed by the end-users while ensuring privacy-preserving. In addition, meta-learning overcomes data availability concerns and speeds up training by utilizing similarly trained tasks. The proposed algorithm is tested using a data set of images of hand-written letters. In addition, we present an uncertainty analysis of the predictions using conformal prediction (CP) techniques. Simulation results show that the proposed Semantic-MSL outperforms conventional schemes by achieving 20 % gain on classification accuracy using fewer data points, yet less training energy consumption.<|reference_end|>
@article{eldeeb2024semantic,
    title={Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless
  Image Classification},
    author={Eslam Eldeeb, Mohammad Shehab, Hirley Alves and Mohamed-Slim Alouini},
    journal={arXiv preprint arXiv:2409.12978},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12978},
    primaryClass={cs.CV cs.LG eess.IV}
}