用分片学习提升医疗物联网安全检测，兼顾隐私与轻量化。
Split Learning-Enabled Framework for Secure and Light-weight Internet of Medical Things Systems
- 将神经网络拆分在设备与边缘服务器间训练，减轻资源受限设备负担。
- 相比主流联邦学习，准确率提升6.35%，通信开销减少33.83%。
- 适合低算力、高隐私要求的医疗物联网场景，如可穿戴设备。
医疗物联网（IoMT）设备的快速增长带来了显著的安全风险，特别是资源受限设备易受恶意软件攻击。传统深度学习因资源限制难以应用，而联邦学习（FL）则存在通信开销高和非独立同分布（non-IID）数据下性能下降的问题。本文提出一种基于分片学习（SL）的IoMT恶意软件检测框架，通过图像分类实现。该框架将神经网络训练过程分布在客户端与边缘服务器之间，既降低了客户端计算负担，又保障了数据隐私。我们采用博弈论方法构建联合优化模型，平衡计算成本与通信效率，以提升训练性能。实验结果表明，所提框架在准确率（+6.35%）、F1分数（+5.03%）、收敛速度（+14.96%）和资源消耗（降低33.83%）方面均优于主流联邦学习方法。这些结果验证了分片学习作为下一代物联网安全可扩展且安全范式的技术潜力。
原文摘要 · Abstract (English)
The rapid growth of Internet of Medical Things (IoMT) devices has resulted in significant security risks, particularly the risk of malware attacks on resource-constrained devices. Conventional deep learning methods are impractical due to resource limitations, while Federated Learning (FL) suffers from high communication overhead and vulnerability to non-IID (heterogeneous) data. In this paper, we propose a split learning (SL) based framework for IoT malware detection through image-based classification. By dividing the neural network training between the clients and an edge server, the framework reduces computational burden on resource-constrained clients while ensuring data privacy. We formulate a joint optimization problem that balances computation cost and communication efficiency by using a game-theoretic approach for attaining better training performance. Experimental evaluations show that the proposed framework outperforms popular FL methods in terms of accuracy (+6.35%), F1-score (+5.03%), high convergence speed (+14.96%), and less resource consumption (33.83%). These results establish the potential of SL as a scalable and secure paradigm for next-generation IoT security.
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