Federated Learning-Empowered Resource Allocation Optimisation for E-Health IoT System

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Original language	English
Title of host publication	2024 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT2024)
DOIs	https://doi.org/10.1109/RFIT60557.2024.10812439
Publication status	Published - 31 Dec 2024

Access to Document

Unmanned Aerial Vehicle Network System Based on Digital Twin Technology with Edge Computing andFederated Learning Process
Bintao Hu (Supervisor), Matilda Isaac (Co-supervisor) & Miguel Lopez-Benitez (Co-supervisor)
1 Jun 2024 → 31 May 2028
Activity: Supervision › PhD Supervision
A hybrid AI algorithm for detecting and preventing security and privacy issues in an IoT network
Bintao Hu (Supervisor), Matilda Isaac (Co-supervisor), Hengyan Liu (Co-supervisor) & Miguel Lopez-Benitez (Co-supervisor)
1 Sept 2023 → 31 Aug 2027
Activity: Supervision › PhD Supervision

@inproceedings{c6f2bba794a447f991040c505db8d7ae,

title = "Federated Learning-Empowered Resource Allocation Optimisation for E-Health IoT System",

author = "Goonjur, {Medhav Kumar} and Zhiran Wang and Matilda Isaac and Hengyan Liu and Shuangyao Huang and Bintao Hu",

year = "2024",

month = dec,

day = "31",

doi = "10.1109/RFIT60557.2024.10812439",

language = "English",

booktitle = "2024 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT2024)",

}

TY - GEN

T1 - Federated Learning-Empowered Resource Allocation Optimisation for E-Health IoT System

AU - Goonjur, Medhav Kumar

AU - Wang, Zhiran

AU - Isaac, Matilda

AU - Liu, Hengyan

AU - Huang, Shuangyao

AU - Hu, Bintao

PY - 2024/12/31

Y1 - 2024/12/31

U2 - 10.1109/RFIT60557.2024.10812439

DO - 10.1109/RFIT60557.2024.10812439

M3 - Conference Proceeding

BT - 2024 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT2024)

ER -