Toward Privacy Preservation in Federated Learning: A Framework Integrating Client-Side Shuffling and Model Compression
- DOI
- 10.1109/tnnls.2026.3690045
- Published
- 2026-11
- Container
- IEEE Transactions on Neural Networks and Learning Systems
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2026.3690045,
title = {Toward Privacy Preservation in Federated Learning: A Framework Integrating Client-Side Shuffling and Model Compression},
author = {Jinguo Li and Ruyang Xiao and Le Yu and Kai Zhang and Jianting Ning},
year = {2026},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
doi = {10.1109/tnnls.2026.3690045},
url = {https://doi.org/10.1109/tnnls.2026.3690045}
}RIS
TY - JOUR TI - Toward Privacy Preservation in Federated Learning: A Framework Integrating Client-Side Shuffling and Model Compression AU - Jinguo Li AU - Ruyang Xiao AU - Le Yu AU - Kai Zhang AU - Jianting Ning PY - 2026 JO - IEEE Transactions on Neural Networks and Learning Systems DO - 10.1109/tnnls.2026.3690045 UR - https://doi.org/10.1109/tnnls.2026.3690045 ER -
APA
Li, J., Xiao, R., Yu, L., Zhang, K., & Ning, J. (2026). Toward Privacy Preservation in Federated Learning: A Framework Integrating Client-Side Shuffling and Model Compression. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2026.3690045
Source records
- crossref · retrieved 2026-11-06T12:28:40.085Z