Develops an SNN-integrated personalized federated learning model for BCI brain-signal analysis in immersive communication, reporting highest identification accuracy and 6.46x lower inference energy than ANN baselines.
Personalized federated learning with moreau envelopes
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Murmura uses epistemic uncertainty from Dirichlet evidential models to score peer compatibility and enable selective trust-aware aggregation for personalized models in decentralized federated learning on non-IID wearable IoT data.
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Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communication
Develops an SNN-integrated personalized federated learning model for BCI brain-signal analysis in immersive communication, reporting highest identification accuracy and 6.46x lower inference energy than ANN baselines.