Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Tang, Minxue"'
Autor:
Tang, Minxue, Wang, Yitu, Zhang, Jingyang, DiValentin, Louis, Ding, Aolin, Hass, Amin, Chen, Yiran, Li, Hai "Helen"
Federated Learning (FL) provides a strong privacy guarantee by enabling local training across edge devices without training data sharing, and Federated Adversarial Training (FAT) further enhances the robustness against adversarial examples, promoting
Externí odkaz:
http://arxiv.org/abs/2409.08372
Autor:
Zhang, Jianyi, Li, Ang, Tang, Minxue, Sun, Jingwei, Chen, Xiang, Zhang, Fan, Chen, Changyou, Chen, Yiran, Li, Hai
Due to limited communication capacities of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training for each communication round. Compared with engaging all the available clients,
Externí odkaz:
http://arxiv.org/abs/2209.15245
Autor:
Tang, Minxue, Zhang, Jianyi, Ma, Mingyuan, DiValentin, Louis, Ding, Aolin, Hassanzadeh, Amin, Li, Hai, Chen, Yiran
Federated adversarial training can effectively complement adversarial robustness into the privacy-preserving federated learning systems. However, the high demand for memory capacity and computing power makes large-scale federated adversarial training
Externí odkaz:
http://arxiv.org/abs/2209.03839
Autor:
Pan, Jingyu, Chang, Chen-Chia, Xie, Zhiyao, Li, Ang, Tang, Minxue, Zhang, Tunhou, Hu, Jiang, Chen, Yiran
Applying machine learning (ML) in design flow is a popular trend in EDA with various applications from design quality predictions to optimizations. Despite its promise, which has been demonstrated in both academic researches and industrial tools, its
Externí odkaz:
http://arxiv.org/abs/2203.16009
Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramatically, the client selection strategy can significantly influence the conve
Externí odkaz:
http://arxiv.org/abs/2103.13822
Modern deep neural networks (DNNs) often require high memory consumption and large computational loads. In order to deploy DNN algorithms efficiently on edge or mobile devices, a series of DNN compression algorithms have been explored, including fact
Externí odkaz:
http://arxiv.org/abs/2004.09031
Hierarchical Reinforcement Learning (HRL) is a promising approach to solving long-horizon problems with sparse and delayed rewards. Many existing HRL algorithms either use pre-trained low-level skills that are unadaptable, or require domain-specific
Externí odkaz:
http://arxiv.org/abs/1910.04450
Akademický článek
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Akademický článek
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Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramatically, the client selection strategy can significantly influence the conve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80b2ded05f8dda5424908030dae16d13
https://doi.org/10.1109/cvpr52688.2022.00986
https://doi.org/10.1109/cvpr52688.2022.00986
