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52. On the Fairness of Swarm Learning in Skin Lesion Classification

Author

Fan, Di, Wu, Yifan, and Li, Xiaoxiao

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computers and Society, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

in healthcare. However, the existing AI model may be biased in its decision marking. The bias induced by data itself, such as collecting data in subgroups only, can be mitigated by including more diversified data. Distributed and collaborative learning is an approach to involve training models in massive, heterogeneous, and distributed data sources, also known as nodes. In this work, we target on examining the fairness issue in Swarm Learning (SL), a recent edge-computing based decentralized machine learning approach, which is designed for heterogeneous illnesses detection in precision medicine. SL has achieved high performance in clinical applications, but no attempt has been made to evaluate if SL can improve fairness. To address the problem, we present an empirical study by comparing the fairness among single (node) training, SL, centralized training. Specifically, we evaluate on large public available skin lesion dataset, which contains samples from various subgroups. The experiments demonstrate that SL does not exacerbate the fairness problem compared to centralized training and improves both performance and fairness compared to single training. However, there still exists biases in SL model and the implementation of SL is more complex than the alternative two strategies.

Published
2021

60. Application of Mobile Intelligent Classroom in Teaching and Analysis of the Teaching Effect—Take the 'Management Accounting Practice' Course as an Example

Author

Fan, Di, Li, Zhou, Peng, Ying, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Yuan, Xueming, editor, Kurniawan, Yohannes, editor, and Ji, Zhenyan, editor

Published
2023
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61. Practical Training Mode Design Under the Background of Informationization Teaching : Taking 'Management Accounting Practice Course' as an Example

Author

Fan, Di, Li, Zhou, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Sun, Jian, editor, Chew, Fong Peng, editor, Khan, Intakhab Alam, editor, and Jenks, Christopher, editor

Published
2023
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62. Empirical Research on Mixed Teaching Effect Based on Multipled Linear Regression Analysis in the Informatization Age : Taking 'The Application of Excel in Finance' as an Example

Author

Li, Zhou, Fan, Di, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Zhan, Zehui, editor, Zhou, Ding, editor, and Wu, Honglin, editor

Published
2023
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68. Lattice thermal conductivity of solid LiF based on machine learning force fields and the Green–Kubo approach.

Author

Li, Si-xuan, Fan, Di, Wang, Jia-chen, Chen, Wen-qian, Song, Hong-zhou, and Lu, Yong

Subjects
*THERMAL conductivity, *MACHINE learning, *LITHIUM fluoride, *MOLECULAR dynamics, *PHONONS, *TANTALUM, *PHONON scattering, *REDSHIFT
Abstract

Obtaining accurate lattice thermal conductivity data of LiF under extreme conditions not only provides important reference for performance evaluation, prediction, and control of materials, but also helps to alleviate the significant challenges of precise experimental measurements. The high-temperature phonon properties and lattice thermal conductivity (LTC) of solid LiF were calculated by combining on-the-fly machine learning force fields (MLFFs) with the Green–Kubo method. The introduction of MLFF successfully combines the accuracy of ab initio molecular dynamics with the scalability advantage of classical molecular dynamics. At high temperatures, there is a significant enhancement in the vibrational coupling between the acoustic and optical branches of LiF, as well as resonant effects between Li and F atoms, resulting in strong anharmonicity. Additionally, the main peak of the phonon density of states shows a noticeable redshift compared to the harmonic case. The enhanced coupling of TO and TA modes at high temperature leads to a significant increase in phonon scattering rate. By considering higher-order phonon anharmonicity, the predicted LTC is significantly reduced compared to the results obtained from considering only three-phonon interactions. Along the Hugoniot curve up to 100 GPa (2150 K), the predicted LTC agrees well with the experimental values. These findings demonstrate the crucial role of phonon anharmonicity in promoting phonon scattering. [ABSTRACT FROM AUTHOR]

Published
2024
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89. DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain

Author

Aggarwal, Garima, Huang, Chun-Yin, Fan, Di, Li, Xiaoxiao, Wang, Zehua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Albarqouni, Shadi, editor, Bakas, Spyridon, editor, Bano, Sophia, editor, Cardoso, M. Jorge, editor, Khanal, Bishesh, editor, Landman, Bennett, editor, Li, Xiaoxiao, editor, Qin, Chen, editor, Rekik, Islem, editor, Rieke, Nicola, editor, Roth, Holger, editor, Sheet, Debdoot, editor, and Xu, Daguang, editor

Published
2022
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94. Inverse Spin Hall Effect Induced by Asymmetric Illumination of Light on Topological Insulator Bi$_2$Se$_3$

Author

Fan, Di, Hobara, Rei, Akiyama, Ryota, and Hasegawa, Shuji

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract

Using circularly polarized light is an alternative to electronic ways for spin injection into materials. Spins are injected at a point of the light illumination, and then diffuse and spread radially due to the in-plane gradient of the spin density. This diffusion is converted into a circular charge current by the inverse spin Hall effect (ISHE). With shining the circularly polarized light at asymmetric parts of the sample, such as near edges, we detected this current as a helicity-dependent component in the photocurrent. We present a model for this ISHE based on the experimental results and the finite-element-method (FEM) simulation of the potential distribution induced by spin injection. Our model shows that the ISHE photocurrent generates an electric dipole at the edge of the sample, causing the measured charge current. The asymmetric light-illumination shown here is a simple way to inject and manipulate spins, opening up a door for novel spintronic devices.

Published
2018
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