Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Huang, Anbu"'
Federated learning (FL) is a new distributed learning paradigm, with privacy, utility, and efficiency as its primary pillars. Existing research indicates that it is unlikely to simultaneously attain infinitesimal privacy leakage, utility loss, and ef
Externí odkaz:
http://arxiv.org/abs/2304.04641
Autor:
Huang, Anbu
Federated Learning (FL) is a new machine learning framework, which enables millions of participants to collaboratively train machine learning model without compromising data privacy and security. Due to the independence and confidentiality of each cl
Externí odkaz:
http://arxiv.org/abs/2011.07429
Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security. Due to model complexity, network unreliability and connection in-stability, co
Externí odkaz:
http://arxiv.org/abs/2001.08600
Autor:
Liu, Yang, Huang, Anbu, Luo, Yun, Huang, He, Liu, Youzhi, Chen, Yuanyuan, Feng, Lican, Chen, Tianjian, Yu, Han, Yang, Qiang
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 highl
Externí odkaz:
http://arxiv.org/abs/2001.06202
Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private. While research efforts on federated learning have been growing tremendously
Externí odkaz:
http://arxiv.org/abs/1910.11089
Autor:
Liu, Yang, Huang, Anbu, Luo, Yun, Huang, He, Liu, Youzhi, Chen, Yuanyuan, Feng, Lican, Chen, Tianjian, Yu, Han, Yang, Qiang
Publikováno v:
AI Magazine; Vol. 42 No. 2: Summer 2021; 19-27
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. T
Autor:
Yang, Qiang, Huang, Anbu, Fan, Lixin, Chan, Chee Seng, Lim, Jian Han, Ng, Kam Woh, Ong, Ding Sheng, Li, Bowen
Publikováno v:
Machine Intelligence Research; February 2023, Vol. 20 Issue: 1 p19-37, 19p
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