用小模型实现少样本无线图像分类，兼顾隐私与效率
Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification
- 分层学习+元学习，端侧计算量小且保护隐私
- 少样本下准确率提升20%，训练能耗更低
- 适合资源受限的物联网图像识别场景
语义导向通信仅传输对任务有意义的信息，但面临终端计算负担重、数据不足和隐私保护等挑战。本文提出一种基于TinyML的语义通信框架，用于少样本无线图像分类，融合分层学习与元学习机制。通过分层学习降低终端侧计算开销并保障隐私；元学习则缓解数据稀缺问题，加速训练过程。实验基于手写字母图像数据集验证，结合共形预测技术进行不确定性分析。仿真结果表明，所提语义-元分拆学习（Semantic-MSL）在使用更少数据点的情况下，分类准确率相比传统方案提升20%，且训练能耗更低。
原文摘要 · Abstract (English)
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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