Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Qianpiao MA"'
Publikováno v:
物联网学报, Vol 7, Pp 1-12 (2023)
In view of the new service scenarios and the demand for high intelligence network in computing and network convergence (CNC), the concept of autonomous CNC (Auto-CNC) is elaborated, where intelligence was introduced into all the aspects of CNC, inclu
Externí odkaz:
https://doaj.org/article/4eaba8c4ac704c95ab1614ac7575c897
Publikováno v:
Tongxin xuebao, Vol 44, Pp 79-93 (2023)
To overcome the three key challenges of federated learning in heterogeneous edge computing, i.e., edge heterogeneity, data Non-IID, and communication resource constraints, a grouping asynchronous federated learning (FedGA) mechanism was proposed.Edge
Externí odkaz:
https://doaj.org/article/3377277cb14c48f7adedcf96f2d4e58e
Publikováno v:
IEEE Transactions on Network Science and Engineering. :1-12
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::422425b4007dba92703497b7117433c3
https://doi.org/10.1109/tnse.2023.3271123
https://doi.org/10.1109/tnse.2023.3271123
Publikováno v:
IEEE Journal on Selected Areas in Communications. 39:3654-3672
Federated learning (FL) involves training machine learning models over distributed edge nodes ( i.e. , workers) while facing three critical challenges, edge heterogeneity, Non-IID data and communication resource constraint. In the synchronous FL, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::393e960d7e33b68d5f0e6bc79d3c079d
https://doi.org/10.1109/jsac.2021.3118435
https://doi.org/10.1109/jsac.2021.3118435
Autor:
Yang Liu, Qianpiao Ma
Publikováno v:
2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA).
With the increasing popularity of the Internet of Things, a huge requirement for low-latency in service response has spurred. Many researches have been proposed on service placement in mobile edge computing (MEC) to address the low-latency requiremen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c6c7f92cc7565e258f603614ec7959a6
https://doi.org/10.1109/icccbda51879.2021.9442568
https://doi.org/10.1109/icccbda51879.2021.9442568
Publikováno v:
INFOCOM
Many important functions in software defined networks can benefit from fine-grained traffic measurement at flow level. Because TCAM-based flow entries only provide aggregate traffic statistics, prior research has suggested to perform flow-level measu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb30878f42827dec5ed38dff297cc40a
https://doi.org/10.1109/infocom.2019.8737516
https://doi.org/10.1109/infocom.2019.8737516