FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing.
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%0 Journal Article
%1 journals/corr/abs-2310-17491
%A Chua, Terence Jie
%A Yu, Wenhan
%A Zhao, Jun
%A Lam, Kwok-Yan
%D 2023
%J CoRR
%K dblp
%T FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing.
%U http://dblp.uni-trier.de/db/journals/corr/corr2310.html#abs-2310-17491
%V abs/2310.17491
@article{journals/corr/abs-2310-17491,
added-at = {2023-11-02T00:00:00.000+0100},
author = {Chua, Terence Jie and Yu, Wenhan and Zhao, Jun and Lam, Kwok-Yan},
biburl = {https://www.bibsonomy.org/bibtex/250337c90f8a257a810bb4c4d8f5e8441/dblp},
ee = {https://doi.org/10.48550/arXiv.2310.17491},
interhash = {a1e98b19dc0e03944df6b23635a35d97},
intrahash = {50337c90f8a257a810bb4c4d8f5e8441},
journal = {CoRR},
keywords = {dblp},
timestamp = {2024-04-08T22:51:41.000+0200},
title = {FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing.},
url = {http://dblp.uni-trier.de/db/journals/corr/corr2310.html#abs-2310-17491},
volume = {abs/2310.17491},
year = 2023
}
