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


  • Yang Liu WeBank
  • Anbu Huang WeBank
  • Yun Luo Extreme Vision Ltd and Hong Kong University of Science and Technology
  • He Huang Extreme Vision Ltd
  • Youzhi Liu WeBank
  • Yuanyuan Chen Nanyang Technological University
  • Lican Feng Extreme Vision Ltd
  • Tianjian Chen WeBank
  • Han Yu Nanyang Technological University
  • Qiang Yang The Hong Kong University of Science and Technology and WeBank




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.




How to Cite

Liu, Y., Huang, A., Luo, Y., Huang, H., Liu, Y., Chen, Y., Feng, L., Chen, T., Yu, H., & Yang, Q. (2020). FedVision: An Online Visual Object Detection Platform Powered by Federated Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 34(08), 13172-13179. https://doi.org/10.1609/aaai.v34i08.7021



IAAI Technical Track: Deployed Papers