Membrane-Aware Dynamic Sparse Training for Federated Spiking Neural Networks

Federated dynamic sparse training has emerged as a promising paradigm for distributed learning with reduced communication overhead. However, existing approaches inherit connection importance estimation criteria from artificial neural networks and overlook the membrane state of postsynaptic neurons, which determines spike emission in spiking neural networks (SNNs). This mismatch leads to inaccurate importance estimation and suboptimal sparse rewiring in federated SNNs. To address this challenge, we propose Membrane-Aware Dynamic Sparse Training (MADST), which introduces three mechanisms: (1) membrane-gated pruning that scores active connections by postsynaptic threshold proximity; (2) activity-guided growth that couples presynaptic spike activity with postsynaptic firing readiness; and (3) support-first server alignment that reconciles heterogeneous client masks under a fixed per-layer density budget. Experiments on CIFAR-10 and CIFAR-100 with Spiking ResNet-18 show that MADST attains the highest accuracy in 10 of the 12 matched-density sparse settings under IID and non-IID conditions, with accuracy gains of up to 1.23 percentage points over the strongest baseline.