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Titlebook: Edge Intelligence in the Making; Optimization, Deep L Sen Lin,Zhi Zhou,Junshan Zhang Book 2021 Springer Nature Switzerland AG 2021

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發(fā)表于 2025-3-21 17:37:09 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Edge Intelligence in the Making
副標(biāo)題Optimization, Deep L
編輯Sen Lin,Zhi Zhou,Junshan Zhang
視頻videohttp://file.papertrans.cn/303/302240/302240.mp4
叢書名稱Synthesis Lectures on Learning, Networks, and Algorithms
圖書封面Titlebook: Edge Intelligence in the Making; Optimization, Deep L Sen Lin,Zhi Zhou,Junshan Zhang Book 2021 Springer Nature Switzerland AG 2021
描述.With the explosive growth of mobile computing and Internet of Things (IoT) applications, as exemplified by AR/VR, smart city, and video/audio surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge.. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of IoT big data. Indeed, the marriage of edge computing and AI has resulted in innovative solutions, namely edge intelligence or edge AI. Nevertheless, research and practice on this emerging inter-disciplinary field is still in its infancy stage. To facilitate the dissemination of the recent advances in edge intelligence in both academia and industry, this book conducts a comprehensive and detailed survey of the recent research efforts and also showcases the authors‘ own research progress on edge intelligence. Specifically, the book first reviewsthe background and present motivation for AI running at the network edge. Next, it provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models towa
出版日期Book 2021
版次1
doihttps://doi.org/10.1007/978-3-031-02380-4
isbn_softcover978-3-031-01252-5
isbn_ebook978-3-031-02380-4Series ISSN 2690-4306 Series E-ISSN 2690-4314
issn_series 2690-4306
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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2690-4306 io surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge.. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential o
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發(fā)表于 2025-3-22 10:41:18 | 只看該作者
https://doi.org/10.1007/978-981-99-8857-0dation, video surveillance, and smart home appliances, which have quickly ascended to the spotlight and gained enormous popularity. It is widely recognized that these intelligent applications are significantly enriching people’s lifes, improving human productivity and enhancing social efficiency.
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Edge Intelligence via Federated Meta-Learning,he knowledge transferred from other edge nodes or the cloud. In this chapter, we focus on collaborative learning across edge nodes, and turn our attention to collaborative learning between the edge and the cloud in next chapter, aiming to fully leverage the potentially valuable knowledge transfer from the cloud.
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發(fā)表于 2025-3-23 05:16:44 | 只看該作者
,China’s Basic Foreign Policy Objectives,he knowledge transferred from other edge nodes or the cloud. In this chapter, we focus on collaborative learning across edge nodes, and turn our attention to collaborative learning between the edge and the cloud in next chapter, aiming to fully leverage the potentially valuable knowledge transfer from the cloud.
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