Multi-FedLinks: Highly reliable decentralized multibiometric federated learning links.
- DOI
- 10.1016/j.neunet.2026.109519
- Published
- 2027 Jan
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
Credibility signals
limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109519,
title = {Multi-FedLinks: Highly reliable decentralized multibiometric federated learning links.},
author = {Guo J and Mu H and Ren H and Han C and Sun L},
year = {2027},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109519},
url = {https://doi.org/10.1016/j.neunet.2026.109519}
}RIS
TY - JOUR TI - Multi-FedLinks: Highly reliable decentralized multibiometric federated learning links. AU - Guo J AU - Mu H AU - Ren H AU - Han C AU - Sun L PY - 2027 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109519 UR - https://doi.org/10.1016/j.neunet.2026.109519 ER -
APA
J, G., H, M., H, R., C, H., & L, S. (2027). Multi-FedLinks: Highly reliable decentralized multibiometric federated learning links.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109519
Source records
- pubmed · retrieved 2026-11-10T14:02:12.097Z