HCFL: hybrid contribution-driven federated learning for fair and efficient optimization
- DOI
- 10.1038/s41598-026-58414-0
- Published
- 2026-07-01
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
Credibility signals
uncertain Score 64/100 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:10.1038/s41598-026-58414-0,
title = {HCFL: hybrid contribution-driven federated learning for fair and efficient optimization},
author = {Younghwan Jeong and Sangshin Lee and Jinyoung Lee and Won Gi Choi},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-58414-0},
url = {https://doi.org/10.1038/s41598-026-58414-0}
}RIS
TY - JOUR TI - HCFL: hybrid contribution-driven federated learning for fair and efficient optimization AU - Younghwan Jeong AU - Sangshin Lee AU - Jinyoung Lee AU - Won Gi Choi PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-58414-0 UR - https://doi.org/10.1038/s41598-026-58414-0 ER -
APA
Jeong, Y., Lee, S., Lee, J., & Choi, W. G. (2026). HCFL: hybrid contribution-driven federated learning for fair and efficient optimization. Scientific Reports. https://doi.org/10.1038/s41598-026-58414-0
Source records
- crossref · retrieved 2026-09-25T15:22:50.228Z