Federated Learning on Multimodal Data: A Comprehensive Survey-Reference-Cited by-同舟云学术

Federated Learning on Multimodal Data: A Comprehensive Survey

Published:2023-06-01 Issue:4 Volume:20 Page:539-553
ISSN:2731-538X
Container-title:Machine Intelligence Research
language:en
Short-container-title:Mach. Intell. Res.

Author:

Lin Yi-Ming^ORCID,Gao Yuan^ORCID,Gong Mao-Guo^ORCID,Zhang Si-Jia,Zhang Yuan-Qiao,Li Zhi-Yuan

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Computer Vision and Pattern Recognition,Modeling and Simulation,Signal Processing,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11633-022-1398-0.pdf

Reference111 articles.

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2. G. A. Kaissis, M. R. Makowski, D. Rückert, R. F. Braren. Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, vol. 2, no. 6, pp. 305–311, 2020. DOI: https://doi.org/10.1038/s42256-020-0186-1.

3. Y. Gao, M. G. Gong, Y. Xie, A. K. Qin, K. Pan, Y. S. Ong. Multiparty dual learning. IEEE Transactions on Cybernetics, published online. DOI: https://doi.org/10.1109/TCYB.2021.3139076.

4. M. J. Sheller, B. Edwards, G. A. Reina, J. Martin, S. Pati, A. Kotrotsou, M. Milchenko, W. L. Xu, D. Marcus, R. R. Colen, S. Bakas. Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, vol. 10, no. 1, Article number 12598, 2020. DOI: https://doi.org/10.1038/s41598-020-69250-1.

5. J. P. Albrecht. How the GDPR will change the world. European Data Protection Law Review, vol. 2, no. 3, pp. 287–289, 2016. DOI: https://doi.org/10.21552/EDPL/2016/3/4.

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