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.
Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connec- tions, stochastic softmax, and hybridization
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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.