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1 Ergebnisse
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Calibre: Towards Fair and Accurate Personalized Federated L..:
, In:
2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)
,
Chen, Sijia
;
Su, Ningxin
;
Li, Baochun
- p. 891-901 , 2024
Link:
https://doi.org/10.1109/ICDCS60910.2024.00087
RT T1 <cTitle>2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)</cTitle>: T1 <Title>Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10630939&Exemplar=1&LAN=DE A1 Chen, Sijia A1 Su, Ningxin A1 Li, Baochun YR 2024 SN 2575-8411 K1 Training K1 Accuracy K1 Federated learning K1 Prototypes K1 Self-supervised learning K1 Data models K1 Data mining K1 Personalized federated learning K1 self-supervised learning K1 model fairness K1 prototype learning SP 891 OP 901 LK http://dx.doi.org/https://doi.org/10.1109/ICDCS60910.2024.00087 DO https://doi.org/10.1109/ICDCS60910.2024.00087 SF ELIB - SuUB Bremen
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