# Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification Source: [Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:21:26.418Z Authors: Eslam Eldeeb, Mohammad Shehab, Hirley Alves, Mohamed-Slim Alouini Published: 2024-09-03T05:56:55.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()