Widely deployed smart cameras are generating a large amount of video data and capable of processing frames on devices. Empowered by edge computing, the video data can also be offloaded to edge servers for processing. By leveraging the on-device processing and computation offloading, we propose a federated video analytics system named FedVision to efficiently provision video analytics across devices and servers. The challenge of designing FedVision is to optimally use the computing and networking resources for video analytics. Since there is no closed-form expression of the system performance, black-box optimization is employed to optimize the system performance. However, using black-box optimization directly incurs excessive system queries that lead to very poor system performance. To solve this problem, we design a new optimization method that integrates black-box optimization with Neural Processes (NPs) as a system performance approximator. This method allows black-box optimizer to query NPs instead of the real system. We validate the performance of FedVision and the new optimization method using both numerical results and experiments with a testbed.

中文翻译：

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FedVision： 边缘计算的联合视频分析

广泛部署的智能相机正在生成大量视频数据，并能够处理设备上的帧。在边缘计算的支持下，视频数据也可以卸载到边缘服务器进行处理。通过利用设备上的处理和计算分流，我们提出了一个联合视频分析系统，名为美联储跨设备和服务器有效地配置视频分析。设计的挑战美联储是为了将计算和网络资源最佳地用于视频分析。由于没有系统性能的封闭形式表示，因此采用黑盒优化来优化系统性能。但是，直接使用黑盒优化会导致过多的系统查询，从而导致非常差的系统性能。为了解决此问题，我们设计了一种新的优化方法，该方法将黑盒优化与神经过程（NP）集成为系统性能近似器。此方法允许黑盒优化程序查询NP，而不是实际系统。我们验证了美联储 以及使用数值结果和试验台进行实验的新优化方法。