Temporal Evidence Beyond Magnitude for Communication-Efficient Federated Spiking Learning

Federated learning with spiking neural networks (SNNs) offers a promising route to energy-efficient edge intelligence, but communication remains a primary bottleneck. Existing compressors largely inherit an artificial neural network (ANN)-oriented view, ranking updates for transmission by their accumulated magnitude. In SNNs, however, each synaptic update accumulates learning contributions across timesteps under stateful leaky integrate-and-fire (LIF) dynamics, so similar update magnitudes can correspond to markedly different temporal contribution patterns. Magnitude-only ranking cannot distinguish these patterns. We introduce LIF temporal evidence, derived from the alignment between timestep-wise learning signals obtained from a single diagnostic replay and the accumulated local update. Building on this evidence, we propose FedBLADE (Block-wise LIF-Aware Dynamic Encoding), which aggregates temporal evidence within architecture- and magnitude-matched blocks, combines it with block update magnitude to prioritize blocks, and admits the highest-ranked blocks under an explicit bit budget, followed by within-block Top-$k$ sparsification, low-bit quantization, and compact relative-position coding. Experiments on Fashion-MNIST, CIFAR-10, and CIFAR-100 under Dirichlet non-IID partitions and uplink budgets of 1–10% of dense transmission show that FedBLADE outperforms representative compression baselines across the tested settings; it also outperforms FedSNN on CIFAR-10 and Fashion-MNIST and achieves competitive performance against several representative federated SNN methods.