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1 Ergebnisse
1
CryptoFE: Practical and Privacy-Preserving Federated Learni..:
, In:
GLOBECOM 2022 - 2022 IEEE Global Communications Conference
,
Qian, Xinyuan
;
Li, Hongwei
;
Hao, Meng
... - p. 2999-3004 , 2022
Link:
https://doi.org/10.1109/GLOBECOM48099.2022.10001080
RT T1 <cTitle>GLOBECOM 2022 - 2022 IEEE Global Communications Conference</cTitle>: T1 <Title>CryptoFE: Practical and Privacy-Preserving Federated Learning via Functional Encryption</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10001080&Exemplar=1&LAN=DE A1 Qian, Xinyuan A1 Li, Hongwei A1 Hao, Meng A1 Yuan, Shuai A1 Zhang, Xilin A1 Guo, Song YR 2022 K1 Training K1 Threat modeling K1 Privacy K1 Federated learning K1 Computational modeling K1 Iron K1 Multi-party computation K1 Privacy-preserving K1 Federated Learning K1 Cloud Computing K1 Multi-Client Functional Encryption SP 2999 OP 3004 LK http://dx.doi.org/https://doi.org/10.1109/GLOBECOM48099.2022.10001080 DO https://doi.org/10.1109/GLOBECOM48099.2022.10001080 SF ELIB - SuUB Bremen
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