FedADAS uses federated distillation to support heterogeneous on-device yawn recognition models across vehicles, delivering up to 9974x lower communication cost than standard federated learning while preserving accuracy under extreme data heterogeneity.
Scientific Reports 14(1), 25029 (2024)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.DC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
FedADAS: Communication-Efficient Federated Distillation for On-Device Driver Yawn Recognition in Vehicular Networks
FedADAS uses federated distillation to support heterogeneous on-device yawn recognition models across vehicles, delivering up to 9974x lower communication cost than standard federated learning while preserving accuracy under extreme data heterogeneity.