提出轻量级语义分片学习框架，兼顾隐私与低功耗
TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation
- 将NLP任务拆分至云端和设备端协同处理，降低本地计算负担
- 相比联邦学习，重建误差提升4倍；相比集中式学习，提升近18倍
- 适合资源受限设备上的隐私保护文本分析场景
自然语言处理任务如语义情感分析和文本生成常引发隐私担忧，并需大量本地计算资源。中心化学习虽节能但需收集原始数据，损害用户隐私；联邦学习虽提升隐私性，却对资源受限设备造成高能耗。本文提出一种节能、隐私保护的微型机器学习（TinyML）框架——语义分片学习（SL），在瑞利衰落与加性噪声环境下，对比联邦学习（FL）和中心化学习（CL）。结果表明，SL显著降低计算功耗与二氧化碳排放，隐私性更强：重建误差较FL高4倍，较CL高近18倍。而联邦学习在隐私与效率间取得较好平衡。代码已开源：https://github.com/AhmedRadwan02/TinyEco2AI-NLP。
原文摘要 · Abstract (English)
Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, often raise privacy concerns and demand significant on-device computational resources. Centralized learning (CL) on the edge provides an energy-efficient alternative but requires collecting raw data, compromising user privacy. While federated learning (FL) enhances privacy, it imposes high computational energy demands on resource-constrained devices. This study provides insights into deploying privacy-preserving, energy-efficient NLP models on edge devices. We introduce semantic split learning (SL) as an energy-efficient, privacy-preserving tiny machine learning (TinyML) framework and compare it to FL and CL in the presence of Rayleigh fading and additive noise. Our results show that SL significantly reduces computational power and CO2 emissions while enhancing privacy, as evidenced by a fourfold increase in reconstruction error compared to FL and nearly eighteen times that of CL. In contrast, FL offers a balanced trade-off between privacy and efficiency. Our code is available for replication at our GitHub repository: https://github.com/AhmedRadwan02/TinyEco2AI-NLP.
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