An Intelligent Doorbell Design Using Federated Deep Learning

被引：1

作者：

Patel, Vatsal ^{[1
]}

Kanani, Sarth ^{[1
]}

Pathak, Tapan ^{[1
]}

Patel, Pankesh ^{[2
]}

Ali, Muhammad Intizar ^{[2
]}

Breslin, John ^{[2
]}

机构：

[1] Pandit Deendayal Petr Univ, Gandhinagar, Gujarat, India

[2] NUI Galway, Data Sci Inst, Confirm SFI Res Ctr Smart Mfg, Galway, Ireland

来源：

CODS-COMAD 2021: PROCEEDINGS OF THE 3RD ACM INDIA JOINT INTERNATIONAL CONFERENCE ON DATA SCIENCE & MANAGEMENT OF DATA (8TH ACM IKDD CODS & 26TH COMAD) | 2021年

关键词：

Federated Learning; Internet of Things; Video Analytics; Artificial Intelligence; Deep Learning; Machine Learning; Privacy; Security;

D O I：

10.1145/3430984.3430988

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：380 / 384

页数：5

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