Federated learning-powered visual object detection for safety monitoring

• Main Authors: Liu, Yang, Huang, Anbu, Luo, Yun, Huang, He, Liu, Youzhi, Chen, Yuanyuan, Feng, Lican, Chen, Tianjian, Yu, Han, Yang, Qiang
• Other Authors: College of Computing and Data Science
• Format: Article
• Language: English
• Published: 2024
• Subjects: Computer and Information Science; Artificial intelligence; Federated learning
• Online Access: https://hdl.handle.net/10356/179045
• Institution: Nanyang Technological University

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 visionbased tasks.