边缘计算(英语:Edge computing),又译为边缘计算,是一种分散式运算的架构,将应用程序、数据资料与服务的运算,由网络中心节点,移往网络逻辑上的边缘节点来处理[1]。边缘运算将原本完全由中心节点处理大型服务加以分解,切割成更小与更容易管理的部分,分散到边缘节点去处理。边缘节点更接近于用户终端装置,可以加快资料的处理与传送速度,减少延迟。在这种架构下,资料的分析与知识的产生,更接近于数据资料的来源,因此更适合处理大数据。

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在许多行业规模的应用中,大量消耗资源的机器学习模型部署在强大的云服务器中。同时,大量的输入数据在云的边缘被收集。推理结果也会传送给用户或传递给位于边缘的下游任务。边缘通常由大量低功率器件组成。如何设计行业产品来支持复杂的深度模型部署和高效地进行模型推理,从而保持较高的模型精度和较低的端到端延迟,是一个巨大的挑战。本文介绍了华为云的边缘云协作原型——Auto-Split背后的技术和工程实践。这项专利技术已经在选定的应用中得到验证,并将用于更广泛的系统边缘云应用集成,并将作为端到端云边缘协同智能部署的自动化管道服务提供给公众使用。据我们所知,目前还没有能够提供深度神经网络(DNN)拆分功能的工业产品。

https://www.zhuanzhi.ai/paper/178ebfb2975bbcc1ef6994f1343d46be

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This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of Federated Learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance of RL while preserving data-privacy. According to the distribution characteristics of the agents in the framework, FRL algorithms can be divided into two categories, i.e. Horizontal Federated Reinforcement Learning (HFRL) and Vertical Federated Reinforcement Learning (VFRL). We provide the detailed definitions of each category by formulas, investigate the evolution of FRL from a technical perspective, and highlight its advantages over previous RL algorithms. In addition, the existing works on FRL are summarized by application fields, including edge computing, communication, control optimization, and attack detection. Finally, we describe and discuss several key research directions that are crucial to solving the open problems within FRL.

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