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论文题目: A Survey on Edge Computing Systems and Tools

论文摘要: 在物联网和5G通信的愿景驱动下,边缘计算系统在网络边缘集成了计算,存储和网络资源,以提供计算基础架构,从而使开发人员能够快速开发和部署边缘应用程序。 如今,边缘计算系统已在业界和学术界引起了广泛关注。 为了探索新的研究机会并帮助用户选择适合特定应用的边缘计算系统,本调查报告对现有边缘计算系统进行了全面概述,并介绍了代表性的项目。 根据开放源代码工具的适用性进行了比较。 最后,我们重点介绍了边缘计算系统的能源效率和深度学习优化。 本次调查还研究了用于分析和设计边缘计算系统的未解决问题。

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The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradients method. In this work, we propose proximal gradient split learning (PSGL) method for defense against the model inversion attacks. The proposed method intentionally attacks the IoMT data when undergoing the deep neural network training process at client side. We propose the use of proximal gradient method to recover gradient maps and a decision-level fusion strategy to improve the recognition performance. Extensive analysis show that the PGSL not only provides effective defense mechanism against the model inversion attacks but also helps in improving the recognition performance on publicly available datasets. We report 17.9$\%$ and 36.9$\%$ gains in accuracy over reconstructed and adversarial attacked images, respectively.

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The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradients method. In this work, we propose proximal gradient split learning (PSGL) method for defense against the model inversion attacks. The proposed method intentionally attacks the IoMT data when undergoing the deep neural network training process at client side. We propose the use of proximal gradient method to recover gradient maps and a decision-level fusion strategy to improve the recognition performance. Extensive analysis show that the PGSL not only provides effective defense mechanism against the model inversion attacks but also helps in improving the recognition performance on publicly available datasets. We report 17.9$\%$ and 36.9$\%$ gains in accuracy over reconstructed and adversarial attacked images, respectively.

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