  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.
