MoFedAGR: Mitigating client drift with adaptive gradient regularization and global momentum in federated learning.
- DOI
- 10.1016/j.neunet.2026.109470
- Published
- 2026 Aug 4
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109470,
title = {MoFedAGR: Mitigating client drift with adaptive gradient regularization and global momentum in federated learning.},
author = {Wang X and Tian L and Gan J and Yang C and Lin F and Li M},
year = {2026},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109470},
url = {https://doi.org/10.1016/j.neunet.2026.109470}
}RIS
TY - JOUR TI - MoFedAGR: Mitigating client drift with adaptive gradient regularization and global momentum in federated learning. AU - Wang X AU - Tian L AU - Gan J AU - Yang C AU - Lin F AU - Li M PY - 2026 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109470 UR - https://doi.org/10.1016/j.neunet.2026.109470 ER -
APA
X, W., L, T., J, G., C, Y., F, L., & M, L. (2026). MoFedAGR: Mitigating client drift with adaptive gradient regularization and global momentum in federated learning.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109470
Source records
- pubmed · retrieved 2026-09-25T17:59:53.722Z