  Spiking federated learning is an emerging distributed learning paradigm that
allows resource-constrained devices to train collaboratively at low power
consumption without exchanging local data. It takes advantage of both the
privacy computation property in federated learning (FL) and the energy
efficiency in spiking neural networks (SNN). Thus, it is highly promising to
revolutionize the efficient processing of multimedia data. However, existing
spiking federated learning methods employ a random selection approach for
client aggregation, assuming unbiased client participation. This neglect of
statistical heterogeneity affects the convergence and accuracy of the global
model significantly. In our work, we propose a credit assignment-based active
client selection strategy, the SFedCA, to judiciously aggregate clients that
contribute to the global sample distribution balance. Specifically, the client
credits are assigned by the firing intensity state before and after local model
training, which reflects the local data distribution difference from the global
model. Comprehensive experiments are conducted on various non-identical and
independent distribution (non-IID) scenarios. The experimental results
demonstrate that the SFedCA outperforms the existing state-of-the-art spiking
federated learning methods, and requires fewer communication rounds.
