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FedFAME: A Data Augmentation Free Framework based on Model ..:
, In:
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
,
Malaviya, Shubham
;
Shukla, Manish
;
Korat, Pratik
. - p. 1114-1121 , 2023
Link:
https://dl.acm.org/doi/10.1145/3555776.3577613
RT T1 <cTitle>Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing</cTitle>: T1 <Title>FedFAME: A Data Augmentation Free Framework based on Model Contrastive Learning for Federated Semi-Supervised Learning</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3577613&Exemplar=1&LAN=DE A1 Malaviya, Shubham A1 Shukla, Manish A1 Korat, Pratik A1 Lodha, Sachin PB ACM YR 2023 K1 federated semi-supervised learning K1 model contrastive learning K1 no data augmentation K1 non-I.I.D. K1 data privacy K1 Computing methodologies K1 Machine learning K1 Learning settings K1 Semi-supervised learning settings K1 Security and privacy K1 Security services K1 Privacy-preserving protocols SP 1114 OP 1121 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3555776.3577613 DO https://dl.acm.org/doi/10.1145/3555776.3577613 SF ELIB - SuUB Bremen
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