Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912036293705728 |
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| author | Eldeeb, Eslam Shehab, Mohammad Alves, Hirley Alouini, Mohamed-Slim |
| author_facet | Eldeeb, Eslam Shehab, Mohammad Alves, Hirley Alouini, Mohamed-Slim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12978 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification Eldeeb, Eslam Shehab, Mohammad Alves, Hirley Alouini, Mohamed-Slim Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 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. |
| title | Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2409.12978 |