FedVision: An Online Visual Object Detection Platform Powered by Federated Learning

TL;DR

FedVision is a pioneering platform that enables easy development of federated learning-based computer vision applications, significantly improving efficiency and privacy in smart city safety monitoring.

Contribution

This paper introduces FedVision, the first practical platform for applying federated learning to computer vision tasks, facilitating non-experts' development of privacy-preserving AI solutions.

Findings

Achieved significant efficiency improvements and cost reductions.

Enabled privacy-preserving data processing for three major customers.

First real-world application of federated learning in computer vision.

Abstract

Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this paper, we report FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.