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1 Ergebnisse
1
MINDFL: Mitigating the Impact of Imbalanced and Noisy-label..:
, In:
MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM)
,
Zhang, Chaoyu
;
Wang, Ning
;
Shi, Shanghao
... - p. 331-338 , 2023
Link:
https://doi.org/10.1109/MILCOM58377.2023.10356215
RT T1 <cTitle>MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM)</cTitle>: T1 <Title>MINDFL: Mitigating the Impact of Imbalanced and Noisy-labeled Data in Federated Learning with Quality and Fairness-Aware Client Selection</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10356215&Exemplar=1&LAN=DE A1 Zhang, Chaoyu A1 Wang, Ning A1 Shi, Shanghao A1 Du, Changlai A1 Lou, Wenjing A1 Hou, Y. Thomas YR 2023 SN 2155-7586 K1 Training K1 Military communication K1 Privacy K1 Federated learning K1 Wireless networks K1 Training data K1 Focusing K1 Quality-of-Model K1 Client Selection K1 Imbalanced and Noisy data SP 331 OP 338 LK http://dx.doi.org/https://doi.org/10.1109/MILCOM58377.2023.10356215 DO https://doi.org/10.1109/MILCOM58377.2023.10356215 SF ELIB - SuUB Bremen
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