Federated Learning-Powered Visual Object Detection for Safety Monitoring

Authors

  • Yang Liu WeBank
  • Anbu Huang WeBank
  • Yun Luo 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 Extreme Vision Ltd
  • Qiang Yang Hong Kong University of Science and Technology

DOI:

https://doi.org/10.1609/aimag.v42i2.15095

Abstract

Visual object detection is an important artificial intelligence (AI) technique for safety monitoring applications. Current approaches for building visual object detection models require large and well-labeled dataset stored by a centralized entity. This not only poses privacy concerns under the General Data Protection Regulation (GDPR), but also incurs large transmission and storage overhead. Federated learning (FL) is a promising machine learning paradigm to address these challenges. In this paper, we report on FedVision—a machine learning engineering platform to support the development of federated learning powered computer vision applications—to bridge this important gap. The platform has been deployed through collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Through actual usage, it has demonstrated significant efficiency improvement and cost reduction while fulfilling privacy-preservation requirements (e.g., reducing communication overhead for one company by 50 fold and saving close to 40,000RMB of network cost per annum). To the best of our knowledge, this is the first practical application of FL in computer vision-based tasks.

Innovative Application of AI, by James Gary

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Published

2021-10-20

How to Cite

Liu, Y., Huang, A., Luo, Y., Huang, H., Liu, Y., Chen, Y. ., Feng, L., Chen, T., Yu, H., & Yang, Q. (2021). Federated Learning-Powered Visual Object Detection for Safety Monitoring. AI Magazine, 42(2), 19-27. https://doi.org/10.1609/aimag.v42i2.15095

Issue

Vol. 42 No. 2: Summer 2021

Section

Special Topic Articles

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