Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communication
This work addresses energy bottlenecks for immersive communication users with neurodiverse brain signals, though it is incremental as it combines existing techniques like SNNs and PFL.
The paper tackles the problem of energy-intensive on-device learning for brain-computer interface-enabled immersive communication by proposing a spiking personalized federated learning model, achieving the best overall identification accuracy and reducing inference energy by 6.46× compared to baselines.
This work proposes a novel immersive communication framework that leverages brain-computer interface (BCI) to acquire brain signals for inferring user-centric states (e.g., intention and perception-related discomfort), thereby enabling more personalized and robust immersive adaptation under strong individual variability. Specifically, we develop a personalized federated learning (PFL) model to analyze and process the collected brain signals, which not only accommodates neurodiverse brain-signal data but also prevents the leakage of sensitive brain-signal information. To address the energy bottleneck of continual on-device learning and inference on energy-limited immersive terminals (e.g., head-mounted display), we further embed spiking neural networks (SNNs) into the PFL. By exploiting sparse, event-driven spike computation, the SNN-enabled PFL reduces the computation and energy cost of training and inference while maintaining competitive personalization performance. Experiments on real brain-signal dataset demonstrate that our method achieves the best overall identification accuracy while reducing inference energy by 6.46$\times$ compared with conventional artificial neural network-based personalized baselines.