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

Original language	English
Title of host publication	2024 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT2024)
Publication status	Accepted/In press - 2024

Projects

• 2 Active

Projects per year

• Development of a Federated Learning-Based Edge Intelligence Framework for IoT Network Systems

  Hu, B., Dong, Y., Isaac, M. & Zhang, W.

  1/07/23 → 30/06/25

  Project: Internal Research Project

• Development of a Federated Learning-Based Edge Intelligence Framework for IoT Network Systems

  Hu, B.

  1/07/23 → 30/06/25

  Project: Internal Research Project

  • Edge Intelligence 100%
  • Federated Learning 100%
  • Internet of Things 100%
  • Linear Programming Problem 33%
  • Mixed-Integer Non-Linear Programming 33%

Activities

• 2 PhD Supervision

Activities per year

• 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

Cite this

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Goonjur, M. K., Wang, Z., Isaac, M., Liu, H., Huang, S., & Hu, B. (Accepted/In press). Federated Learning-Empowered Resource Allocation Optimisation for E-Health IoT System. In 2024 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT2024)

Goonjur, Medhav Kumar ; Wang, Zhiran ; Isaac, Matilda et al. / Federated Learning-Empowered Resource Allocation Optimisation for E-Health IoT System. 2024 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT2024). 2024.

@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",

language = "English",

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

}

Goonjur, MK, Wang, Z, Isaac, M, Liu, H, Huang, S & Hu, B 2024, Federated Learning-Empowered Resource Allocation Optimisation for E-Health IoT System. in 2024 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT2024).

Federated Learning-Empowered Resource Allocation Optimisation for E-Health IoT System. / Goonjur, Medhav Kumar; Wang, Zhiran; Isaac, Matilda et al.
2024 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT2024). 2024.

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

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

Y1 - 2024

M3 - Conference Proceeding

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

ER -

Goonjur MK, Wang Z, Isaac M, Liu H, Huang S, Hu B. Federated Learning-Empowered Resource Allocation Optimisation for E-Health IoT System. In 2024 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT2024). 2024