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.
IEEE Transactions on Cybernetics52(12), 13821–13833 (2021)
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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.