# Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communication

arXiv: https://arxiv.org/abs/2603.22727 · HTML: https://mlsanity.com/paper/2603.22727/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Federated Learning with Spiking Neural Networks](https://mlsanity.com/paper/2106.06579/) (2021)
- [AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices](https://mlsanity.com/paper/2301.03062/) (2023)
- [A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey](https://mlsanity.com/paper/2309.01848/) (2023)
- [Energy-Efficient and Intelligent ISAC in V2X Networks with Spiking Neural Networks-Driven DRL](https://mlsanity.com/paper/2501.01038/) (2025)
- [Enhancing Immersion and Presence in the Metaverse with Over-the-Air Brain-Computer Interface](https://mlsanity.com/paper/2303.10577/) (2023)
- [Integrating Brain-Computer Interface and Neuromorphic Computing for Human Digital Twins](https://mlsanity.com/paper/2410.23639/) (2024)

## Abstract (arXiv)

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× compared with conventional artificial neural network-based personalized baselines.