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1 Ergebnisse
1
FedADP: Communication-Efficient by Model Pruning for Federa..:
, In:
GLOBECOM 2023 - 2023 IEEE Global Communications Conference
,
Liu, Haiyang
;
Shi, Yuliang
;
Su, Zhiyuan
... - p. 3093-3098 , 2023
Link:
https://doi.org/10.1109/GLOBECOM54140.2023.10437081
RT T1 <cTitle>GLOBECOM 2023 - 2023 IEEE Global Communications Conference</cTitle>: T1 <Title>FedADP: Communication-Efficient by Model Pruning for Federated Learning</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10437081&Exemplar=1&LAN=DE A1 Liu, Haiyang A1 Shi, Yuliang A1 Su, Zhiyuan A1 Zhang, Kun A1 Wang, Xinjun A1 Yan, Zhongmin A1 Kong, Fanyu YR 2023 SN 2576-6813 K1 Training K1 Adaptation models K1 Costs K1 Federated learning K1 Computational modeling K1 Data models K1 Servers K1 federated learning K1 communication-efficient K1 model compression SP 3093 OP 3098 LK http://dx.doi.org/https://doi.org/10.1109/GLOBECOM54140.2023.10437081 DO https://doi.org/10.1109/GLOBECOM54140.2023.10437081 SF ELIB - SuUB Bremen
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