Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine.
- DOI
- 10.1109/tase.2026.3660098
- Published
- 2026
- Container
- IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1109/tase.2026.3660098,
title = {Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine.},
author = {Ding Y and Costa C and Si B},
year = {2026},
journal = {IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society},
doi = {10.1109/tase.2026.3660098},
url = {https://doi.org/10.1109/tase.2026.3660098}
}RIS
TY - JOUR TI - Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine. AU - Ding Y AU - Costa C AU - Si B PY - 2026 JO - IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society DO - 10.1109/tase.2026.3660098 UR - https://doi.org/10.1109/tase.2026.3660098 ER -
APA
Y, D., C, C., & B, S. (2026). Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine.. IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society. https://doi.org/10.1109/tase.2026.3660098
Source records
- pubmed · retrieved 2026-09-25T16:44:32.469Z