A unified basis decomposition framework for addressing the federated learning trilemma: Communication-efficiency, personalization, and privacy.
- DOI
- 10.1016/j.neunet.2026.109378
- Published
- 2027 Jan
- 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.2026.109378,
title = {A unified basis decomposition framework for addressing the federated learning trilemma: Communication-efficiency, personalization, and privacy.},
author = {Ma Z and Wu Z and Wang J and Zhu Y and Gao X and Lin Y and Wang J and Lu W},
year = {2027},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109378},
url = {https://doi.org/10.1016/j.neunet.2026.109378}
}RIS
TY - JOUR TI - A unified basis decomposition framework for addressing the federated learning trilemma: Communication-efficiency, personalization, and privacy. AU - Ma Z AU - Wu Z AU - Wang J AU - Zhu Y AU - Gao X AU - Lin Y AU - Wang J AU - Lu W PY - 2027 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109378 UR - https://doi.org/10.1016/j.neunet.2026.109378 ER -
APA
Z, M., Z, W., J, W., Y, Z., X, G., Y, L., J, W., & W, L. (2027). A unified basis decomposition framework for addressing the federated learning trilemma: Communication-efficiency, personalization, and privacy.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109378
Source records
- pubmed · retrieved 2026-09-25T23:26:21.731Z