Federated Graph Neural Networks: Overview, Techniques, and Challenges-Reference-Cited by-同舟云学术

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Federated Graph Neural Networks: Overview, Techniques, and Challenges

Published:2024 Issue: Volume: Page:1-17
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Liu Rui¹^ORCID,Xing Pengwei¹^ORCID,Deng Zichao¹,Li Anran¹^ORCID,Guan Cuntai¹^ORCID,Yu Han¹^ORCID

Affiliation:

1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

Funder

National Research Foundation, Singapore, and Defence Science Organisation (DSO) National Laboratories through the AI Singapore Programme

Nanyang Technological University (NTU), Singapore

RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund, Singapore

Nanyang Technological University through the Nanyang Assistant Professorship

Future Communications Research and Development Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10428063.pdf?arnumber=10428063

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1. HHGNN: Hyperbolic Hypergraph Convolutional Neural Network based on variational autoencoder;Neurocomputing;2024-10

2. Obtaining and qualitative analysis of time-lagged correlations between seawater quality parameters;Measurement Science and Technology;2024-09-06

3. Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review;2024-08-09

4. Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks;Sensors;2024-08-08

5. Mitigating data imbalance and generating better prototypes in heterogeneous Federated Graph Learning;Knowledge-Based Systems;2024-07

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