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13. Device-Performance-Driven Heterogeneous Multiparty Learning for Arbitrary Images

Author

Zhang, Yuanqiao, Gong, Maoguo, Gao, Yuan, Qin, A. K., Wang, Kun, Lin, Yiming, and Wang, Shanfeng

Abstract

Multiparty learning (MPL) is an emerging framework for privacy-preserving collaborative learning. It enables individual devices to build a knowledge-shared model and remaining sensitive data locally. However, with the continuous increase of users, the heterogeneity gap between data and equipment becomes wider, which leads to the problem of model heterogeneous. In this article, we concentrate on two practical issues: data heterogeneous problem and model heterogeneous problem, and propose a novel personal MPL method named device-performance-driven heterogeneous MPL (HMPL). First, facing the data heterogeneous problem, we focus on the problem of various devices holding arbitrary data sizes. We introduce a heterogeneous feature-map integration method to adaptively unify the various feature maps. Meanwhile, to handle the model heterogeneous problem, as it is essential to customize models for adapting to the various computing performances, we propose a layer-wise model generation and aggregation strategy. The method can generate customized models based on the device’s performance. In the aggregation process, the shared model parameters are updated through the rules that the network layers with the same semantics are aggregated with each other. Extensive experiments are conducted on four popular datasets, and the result demonstrates that our proposed framework outperforms the state of the art (SOTA).

Published
2024
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16. Limited‐angle tomography reconstruction with a U‐Conv‐Swin‐Net.

Author

Fu, Tianyu, Qiu, Sen, Wang, Yan, Zhang, Kai, Zhou, Chenpeng, Zhang, Jin, Wang, Shanfeng, Huang, Wanxia, Tao, Ye, and Yuan, Qingxi

Subjects
COMPUTED tomography, TOMOGRAPHY, TRANSFORMER models, X-ray imaging
Abstract

X‐ray computed tomography (CT) is widely used as a non‐destructive inspection technology. However, due to various limitations such as sample dimensions and blocking of in‐situ instruments, projections can be acquired only in a limited‐angle range during CT acquisition. When such projections are reconstructed with conventional algorithms, the details and contours are deteriorated by artefacts due to lack of information at certain view angles. To address this problem, we propose a reconstruction method based on U‐Conv‐Swin‐Net (UCSN) in this paper. Validated through synthetic and experimental data, the proposed method can effectively remove the degrading artefacts and fully restore the sample details and edges. In addition, the UCSN method exhibits superior reconstruction effect for different scanning ranges compared with conventional reconstruction methods. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Introduction

Author

Gong, Maoguo, Cai, Qing, Ma, Lijia, Wang, Shanfeng, Lei, Yu, Gong, Maoguo, Cai, Qing, Ma, Lijia, Wang, Shanfeng, and Lei, Yu

Published
2017
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30. Nanoscale analysis of magnetic chains consisting of cobalt mesospheres by full‐field transmission x‐ray tomography.

Author

Zhang, Jin, Zhou, Chenpeng, Huang, Wanxia, Wang, Shanfeng, and Wang, Yan

Subjects
TOMOGRAPHY, NANOSTRUCTURED materials, COBALT, IMAGE analysis, MATERIALS analysis
Abstract

Full‐field transmission x‐ray tomography can obtain 3D morphology and configurations of nanoscale materials. In this study, we further demonstrate the feasibility of this technique using magnetic chains consisting of cobalt mesospheres. The preliminary images presented in this article clearly reveal that three types of cobalt nanoparticles, namely, hollow, core–shell, and solid were found for magnetic chain synthesis. Furthermore, the tomographic analyses revealed the growth mechanism of magnetic chains self‐assembled by cobalt nanoparticles. We may claim that the spatial resolution and contrast characteristics of the full‐field transmission x‐ray tomography can satisfy the research requirements of nanomaterial science. Hence, transmission x‐ray tomography could be a powerful complementary method for imaging and analysis of nanoscale materials. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Concluding Remarks

Author

Gong, Maoguo, Cai, Qing, Ma, Lijia, Wang, Shanfeng, Lei, Yu, Gong, Maoguo, Cai, Qing, Ma, Lijia, Wang, Shanfeng, and Lei, Yu

Published
2017
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35. Improved OBS-NMF Algorithm for Intrusion Detection

Author

Ma, Wenping, Wu, Yue, Wang, Shanfeng, Gong, Maoguo, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, He, Cheng, editor, Mo, Hongwei, editor, Pan, Linqiang, editor, and Zhao, Yuxin, editor

Published
2017
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36. Parallel Particle Swarm Optimization for Community Detection in Large-Scale Networks

Author

Wang, Shanfeng, Gong, Maoguo, Wu, Yue, Qin, Xiaolei, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Shi, Yuhui, editor, Tan, Kay Chen, editor, Zhang, Mengjie, editor, Tang, Ke, editor, Li, Xiaodong, editor, Zhang, Qingfu, editor, Tan, Ying, editor, Middendorf, Martin, editor, and Jin, Yaochu, editor

Published
2017
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41. Multi-objective Evolutionary Algorithm for Enhancing the Robustness of Networks

Author

Li, Zheng, Wang, Shanfeng, Ma, Wenping, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Gong, Maoguo, editor, Pan, Linqiang, editor, Song, Tao, editor, and Zhang, Gexiang, editor

Published
2016
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46. Four‐In‐One Strategy to Boost the Performance of Na x [Ni,Mn]O 2

Author

Zhang, Shiguang, primary, Li, Xinyan, additional, Su, Yun, additional, Yang, Yang, additional, Yu, Hao, additional, Wang, Haibo, additional, Zhang, Qing, additional, Lu, Yaxiang, additional, Song, Dawei, additional, Wang, Shanfeng, additional, Zhang, Qinghua, additional, Rong, Xiaohui, additional, Zhang, Lianqi, additional, Chen, Liquan, additional, and Hu, Yong‐Sheng, additional

Published
2023
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