Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144326
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dc.contributor.authorLiu, Yangen_US
dc.contributor.authorHuang, Anbuen_US
dc.contributor.authorLuo, Yunen_US
dc.contributor.authorHuang, Heen_US
dc.contributor.authorLiu, Youzhien_US
dc.contributor.authorChen, Yuanyuanen_US
dc.contributor.authorFeng, Licanen_US
dc.contributor.authorChen, Tianjianen_US
dc.contributor.authorYu, Hanen_US
dc.contributor.authorYang, Qiangen_US
dc.date.accessioned2020-10-28T07:24:54Z-
dc.date.available2020-10-28T07:24:54Z-
dc.date.issued2020-
dc.identifier.citationLiu, Y., Huang, A., Luo, Y., Huang, H., Liu, Y., Chen, Y., ... Yang, Q. (2020). FedVision : an online visual object detection platform powered by federated learning. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 13172-13179. doi:10.1609/aaai.v34i08.7021en_US
dc.identifier.urihttps://hdl.handle.net/10356/144326-
dc.description.abstractVisual 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.en_US
dc.description.sponsorshipAI Singaporeen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.rights© 2020 Association for the Advancement of Artificial Intelligence (AAAI). All rights reserved. This paper was published in Proceedings of the AAAI Conference on Artificial Intelligence and is made available with permission of Association for the Advancement of Artificial Intelligence (AAAI).en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleFedVision : an online visual object detection platform powered by federated learningen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conferenceAAAI Conference on Artificial Intelligenceen_US
dc.identifier.doi10.1609/aaai.v34i08.7021-
dc.description.versionAccepted versionen_US
dc.identifier.volume34en_US
dc.identifier.spage13172en_US
dc.identifier.epage13179en_US
dc.subject.keywordsVisual Object Detectionen_US
dc.subject.keywordsArtificial Intelligenceen_US
dc.description.acknowledgementThis research is supported by the Nanyang Assistant Professorship (NAP); the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-GC-2019-003); the National Research Foundation, Singapore, Prime Minister’s Office under its NRF Investigatorship Programme (NRFI Award No: NRF-NRFI05- 2019-0002); the Joint NTU-WeBank Research Centre on Fintech (NWJ-2019-007), Nanyang Technological University, Singapore; and the R&D group of Extreme Vision Ltd, Shenzhen, China. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.en_US
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