Spatio-temporal vertical federated learning to overcome data sharing limitations

Category

Published in:

Abstracts of the ICA
Vol. 7.2024 (02.09.2024), art. no. 95, p. 1-2

Volume:

7.2024

Publication date:

02.09.2024

URN:

urn:nbn:de:0307-20250911-006-0

DOI:

10.5194/ica-abs-7-95-2024

Language:

English

Type of Resource:

Text

Extent:

1 Online-Ressource (Seite 1-2, 717,97 kB) : 1 Illustration, 1 Textdatei (PDF)

Keywords:

Machine Learning; Federated Learning; GeoAI; Maschinelles Lernen^GND; föderales Lernen; GeoKI

DDC subject of DNB:

520 Astronomy & allied sciences

URL:

https://doi.org/10.5194/ica-abs-7-95-2024

URL:

https://ica-abs.copernicus.org/articles/7/95/2024/

Link to Raw Object:

https://ica-abs.copernicus.org/articles/7/95/2024/ica-abs-7-95-2024.pdf

Statement of responsibility:

Jose Antonio Lorencio Abril, Anita Graser, Axel Weißenfeld, Anahid Wachsenegger

Bibliographic notes:

Published in: Abstracts of the ICA, Volume 7, 2024 - European Cartographic Conference – EuroCarto 2024; 9–11 September 2024, TU Wien, Vienna, Austria; Editors: Georg Gartner, Florian Ledermann, and Andrea Binn | https://ica-abs.copernicus.org/articles/7/index.html