ESA-FedGNN: Efficient secure aggregation for federated graph neural networks

Yanjun Liu, Hongwei Li, Xinyuan Qian, Meng Hao

Open source

DOI: 10.1007/s12083-023-01472-2
Published: 2023-03
Container: Peer-to-Peer Networking and Applications
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1007/s12083-023-01472-2,
  title = {ESA-FedGNN: Efficient secure aggregation for federated graph neural networks},
  author = {Yanjun Liu and Hongwei Li and Xinyuan Qian and Meng Hao},
  year = {2023},
  journal = {Peer-to-Peer Networking and Applications},
  doi = {10.1007/s12083-023-01472-2},
  url = {https://doi.org/10.1007/s12083-023-01472-2}
}

RIS

TY  - JOUR
TI  - ESA-FedGNN: Efficient secure aggregation for federated graph neural networks
AU  - Yanjun Liu
AU  - Hongwei Li
AU  - Xinyuan Qian
AU  - Meng Hao
PY  - 2023
JO  - Peer-to-Peer Networking and Applications
DO  - 10.1007/s12083-023-01472-2
UR  - https://doi.org/10.1007/s12083-023-01472-2
ER  -

APA

Liu, Y., Li, H., Qian, X., & Hao, M. (2023). ESA-FedGNN: Efficient secure aggregation for federated graph neural networks. Peer-to-Peer Networking and Applications. https://doi.org/10.1007/s12083-023-01472-2

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crossref · retrieved 2026-09-25T19:10:15.628Z