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1 Ergebnisse
1
How Few Davids Improve One Goliath: Federated Learning in R..:
, In:
Proceedings of the ACM Web Conference 2024
,
Zhang, Jiayun
;
Li, Shuheng
;
Huang, Haiyu
... - p. 2976-2985 , 2024
Link:
https://dl.acm.org/doi/10.1145/3589334.3645544
RT T1
Proceedings of the ACM Web Conference 2024
: T1
How Few Davids Improve One Goliath: Federated Learning in Resource-Skewed Edge Computing Environments
UL https://suche.suub.uni-bremen.de/peid=acm-3645544&Exemplar=1&LAN=DE A1 Zhang, Jiayun A1 Li, Shuheng A1 Huang, Haiyu A1 Wang, Zihan A1 Fu, Xiaohan A1 Hong, Dezhi A1 Gupta, Rajesh K. A1 Shang, Jingbo PB ACM YR 2024 K1 edge computing K1 federated learning K1 system heterogeneity K1 Computing methodologies K1 Artificial intelligence K1 Distributed artificial intelligence K1 Human-centered computing K1 Ubiquitous and mobile computing K1 Machine learning SP 2976 OP 2985 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3589334.3645544 DO https://dl.acm.org/doi/10.1145/3589334.3645544 SF ELIB - SuUB Bremen
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