FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
By Yang Liu, Anbu Huang, Yu Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Hang Yu, Qiang Yang. 2020
352 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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Citations in the corpus
- FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “Promising application domains include, but are not limited to, computer vision [77, 78], natural language processing [79, 80, 81, 82, 83], finance [74, 35, 84], transportation [85, 86, 87, 88, 89, 90, 91, 92, 93, 94], digital health [95, 96, 97, 98, 99, 100, 101], recommendation [102, 103, 104, 105,…”
- From distributed machine learning to federated learning: a survey (2021, background): “, Natural Language Processing (NLP), Computing Vision (CV) [86], and recommendation.”
- Model-Contrastive Federated Learning (2021, methodology): “Moreover, it has been applied in many applications such as medical imaging [21, 23], object detection [32], and landmark classification [15].”
- Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “urban communication, social economy sharing, social activity monitoring, and interconnection of global citizens [154].”
- Threats to Federated Learning: A Survey (2020, background): “In recent years, FL has benefited a wide range of applications such as next word prediction [ McMahan et al. , 2016a; McMahan et al. , 2018 ] , visual object detection for safety [ Liu et al. , 2020 ] , etc.”
- Learn from Others and Be Yourself in Heterogeneous Federated Learning (2022, background): “It has been an active and challenging research topic and shows promising results in real-world setting [17, 19, 29, 52, 54].”
- Privacy and Robustness in Federated Learning: Attacks and Defenses (2020, background): “In recent years, FL has benefited a wide range of applications such as next word prediction [6], [7], visual object detection for safety [8], entity resolution [9], etc.”
- FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection (2021, background): “…into FATE, building light-weight versions of FATE for edge deployment and applications, and building new applications with FATE in industrial scenarios such as computer vision (Liu et al., 2020) and automatic speech-recognition (ASR) (Jiang et al., 2021) to further enable federated AI technologies.”