Efficient Resource Slicing Scheme for Optimizing Federated Learning Communications in Software-Defined IoT Networks
Prohim Tam, Sa Math, Seokhoon Kim, Journal of Internet Computing and Services, Vol. 22, No. 5, pp. 27-33, Oct. 2021
10.7472/jksii.2021.22.5.27, Full Text:
Keywords: Deep Learning, Federated learning, Internet of Things, Network Functions Virtualization, Software-Defined Networking
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Cite this article
[APA Style]
Tam, P., Math, S., & Kim, S. (2021). Efficient Resource Slicing Scheme for Optimizing Federated Learning Communications in Software-Defined IoT Networks. Journal of Internet Computing and Services, 22(5), 27-33. DOI: 10.7472/jksii.2021.22.5.27.
[IEEE Style]
P. Tam, S. Math, S. Kim, "Efficient Resource Slicing Scheme for Optimizing Federated Learning Communications in Software-Defined IoT Networks," Journal of Internet Computing and Services, vol. 22, no. 5, pp. 27-33, 2021. DOI: 10.7472/jksii.2021.22.5.27.
[ACM Style]
Prohim Tam, Sa Math, and Seokhoon Kim. 2021. Efficient Resource Slicing Scheme for Optimizing Federated Learning Communications in Software-Defined IoT Networks. Journal of Internet Computing and Services, 22, 5, (2021), 27-33. DOI: 10.7472/jksii.2021.22.5.27.