Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification

• URL: http://arxiv.org/abs/2409.12978v1
• Date: Tue, 3 Sep 2024 05:56:55 GMT
• Title: Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification
• Authors: Eslam Eldeeb, Mohammad Shehab, Hirley Alves, Mohamed-Slim Alouini,
• Abstract summary: This work presents a TinyML-based semantic communication framework for few-shot wireless image classification. We exploit split-learning to limit the computations performed by the end-users while ensuring privacy-preserving. meta-learning overcomes data availability concerns and speeds up training by utilizing similarly trained tasks.
• Score: 50.28867343337997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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