Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Yaguan Qian"'
Publikováno v:
Electronic Research Archive, Vol 31, Iss 11, Pp 7021-7039 (2023)
A key issue in current federated learning research is how to improve the performance of federated learning algorithms by reducing communication overhead and computing costs while ensuring data privacy. This paper proposed an efficient wireless transm
Externí odkaz:
https://doaj.org/article/10035946918e419e9c90b579e0d8680e
Publikováno v:
International Journal of Engineering Technologies and Management Research. 10:34-49
Federated learning can effectively utilize data from various users to coordinately train machine learning models while ensuring that data does not leave the user's device. However, it also faces the challenge of slow global model convergence and even
Publikováno v:
European Journal of Operational Research. 304:577-595
Autor:
Yaguan Qian, Zhiqiang He, Yuqi Wang, Bin Wang, Xiang Ling, Zhaoquan Gu, Haijiang Wang, Shaoning Zeng, Wassim Swaileh
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-13
Publikováno v:
Neurocomputing. 500:135-142
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:3571-3579
RGB–thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extraction for prediction maps and cannot fully use high-level features. In
Autor:
Yaguan Qian, Yankai Guo, Qiqi Shao, Jiamin Wang, Bin Wang, Zhaoquan Gu, Xiang Ling, Chunming Wu
Publikováno v:
ACM Transactions on Privacy and Security. 25:1-24
Edge intelligence has played an important role in constructing smart cities, but the vulnerability of edge nodes to adversarial attacks becomes an urgent problem. A so-called adversarial example can fool a deep learning model on an edge node for misc
Publikováno v:
International Journal of Pattern Recognition and Artificial Intelligence. 36
Adversarial training is by far one of the most effective methods to improve the robustness of deep neural networks against adversarial examples. However, the trade-off between robustness and accuracy is still a challenge in adversarial training. Prev
Publikováno v:
International Journal of Theoretical Physics. 60:2668-2682
Federated Learning (FL) is a promising paradigm, where the local users collaboratively learn models by repeatedly sharing information while the data is kept distributing on these users. FL has been considered in multiple access channels (FL-MAC), whi
Publikováno v:
Interspeech 2022.