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.
In: Artificial intelligence and statistics
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
CELM uses class-wise evidence scores from client logits to compute contribution weights that upweight clients strong on underrepresented classes for stable aggregation in non-IID federated learning.
AdaBFL introduces a novel three-layer adaptive aggregation mechanism for Byzantine-robust federated learning that counters complex attacks, provides non-convex non-iid convergence guarantees, and shows superior performance in experiments.
citing papers explorer
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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.
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Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning
CELM uses class-wise evidence scores from client logits to compute contribution weights that upweight clients strong on underrepresented classes for stable aggregation in non-IID federated learning.
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AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning
AdaBFL introduces a novel three-layer adaptive aggregation mechanism for Byzantine-robust federated learning that counters complex attacks, provides non-convex non-iid convergence guarantees, and shows superior performance in experiments.