Federated learning vector quantization for dealing with drift between nodes
Vaquet V, Hinder F, Brinkrolf J, Menz P, Seiffert U, Hammer B (Accepted)
Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.
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Autor*in
Vaquet, ValerieUniBi 
;
Hinder, FabianUniBi;
Brinkrolf, JohannesUniBi ;
Menz, Patrick;
Seiffert, Udo;
Hammer, BarbaraUniBi
;
Hinder, FabianUniBi;
Brinkrolf, JohannesUniBi ;
Menz, Patrick;
Seiffert, Udo;
Hammer, BarbaraUniBi Projekt
Erscheinungsjahr
2022
Konferenz
30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022
Konferenzort
Bruges
Page URI
https://pub.uni-bielefeld.de/record/2964534
Zitieren
Vaquet V, Hinder F, Brinkrolf J, Menz P, Seiffert U, Hammer B. Federated learning vector quantization for dealing with drift between nodes. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.
Vaquet, V., Hinder, F., Brinkrolf, J., Menz, P., Seiffert, U., & Hammer, B. (Accepted). Federated learning vector quantization for dealing with drift between nodes. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.
Vaquet, Valerie, Hinder, Fabian, Brinkrolf, Johannes, Menz, Patrick, Seiffert, Udo, and Hammer, Barbara. Accepted. “Federated learning vector quantization for dealing with drift between nodes”. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges .
Vaquet, V., Hinder, F., Brinkrolf, J., Menz, P., Seiffert, U., and Hammer, B. (Accepted).“Federated learning vector quantization for dealing with drift between nodes”. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.
Vaquet, V., et al., Accepted. Federated learning vector quantization for dealing with drift between nodes. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.
V. Vaquet, et al., “Federated learning vector quantization for dealing with drift between nodes”, Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges, Accepted.
Vaquet, V., Hinder, F., Brinkrolf, J., Menz, P., Seiffert, U., Hammer, B.: Federated learning vector quantization for dealing with drift between nodes. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges (Accepted).
Vaquet, Valerie, Hinder, Fabian, Brinkrolf, Johannes, Menz, Patrick, Seiffert, Udo, and Hammer, Barbara. “Federated learning vector quantization for dealing with drift between nodes”. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges, Accepted.
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