BESplit mitigates non-IID bias in split federated learning via evidential aggregation, bias-compensated client pairing, and dual-teacher distillation, outperforming prior methods on five benchmarks.
Journal of the Royal Statistical Society: Series B (Methodological) , volume=
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Plug-in losses approximate EDL training objectives at the Dirichlet mean with decaying error as evidence grows, including softmax under a specific mapping, and match classical EDL performance on Google Speech Commands.
ConfSleepNet introduces a conflict-aware evidential aggregation method for multi-modal sleep stage classification using hybrid category structures per modality to produce reliable joint decisions with uncertainty.
citing papers explorer
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BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
BESplit mitigates non-IID bias in split federated learning via evidential aggregation, bias-compensated client pairing, and dual-teacher distillation, outperforming prior methods on five benchmarks.
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Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier
Plug-in losses approximate EDL training objectives at the Dirichlet mean with decaying error as evidence grows, including softmax under a specific mapping, and match classical EDL performance on Google Speech Commands.
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A Conflict-aware Evidential Framework for Reliable Sleep Stage Classification
ConfSleepNet introduces a conflict-aware evidential aggregation method for multi-modal sleep stage classification using hybrid category structures per modality to produce reliable joint decisions with uncertainty.
- Possibilistic Predictive Uncertainty for Deep Learning