论文标题

使用联邦深度学习的智能门铃设计的演示

A Demonstration of Smart Doorbell Design Using Federated Deep Learning

论文作者

Patel, Vatsal, Kanani, Sarth, Pathak, Tapan, Patel, Pankesh, Ali, Muhammad Intizar, Breslin, John

论文摘要

智能门铃在保护我们的现代房屋方面发挥了重要作用。将视频流发送到视频分析的集中式服务器(或云)的现有方法一直面临许多挑战,例如延迟,带宽成本以及更重要的是用户的隐私问题。为了应对这些挑战,本文展示了基于联合深度学习的智能智能门铃的能力,该智能门铃可以部署和管理视频分析应用程序,例如跨越边缘的智能门铃和云资源。该平台可以扩展,与多个设备一起使用,无缝管理应用程序组件的在线编排。提出的框架是使用最先进的技术实施的。我们使用烧瓶框架实现了联合服务器,该框架框架使用Nginx和Gunicorn进行了容器,该框架已部署在AWS EC2和AWS无服务器体系结构上。

Smart doorbells have been playing an important role in protecting our modern homes. Existing approaches of sending video streams to a centralized server (or Cloud) for video analytics have been facing many challenges such as latency, bandwidth cost and more importantly users' privacy concerns. To address these challenges, this paper showcases the ability of an intelligent smart doorbell based on Federated Deep Learning, which can deploy and manage video analytics applications such as a smart doorbell across Edge and Cloud resources. This platform can scale, work with multiple devices, seamlessly manage online orchestration of the application components. The proposed framework is implemented using state-of-the-art technology. We implement the Federated Server using the Flask framework, containerized using Nginx and Gunicorn, which is deployed on AWS EC2 and AWS Serverless architecture.

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