SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning

The spiking federated learning (FL) 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 FL and the energy efficiency in spiking neural networks (SNNs). However, existing spiking FL methods employ a random selection approach for client aggregation, assuming unbiased client participation. This neglect of statistical heterogeneity significantly affects the convergence and precision of the global model. In this work, we propose a credit assignment-based active client selection strategy for spiking federated learning, the SFedCA, to aggregate clients contributing to the global sample distribution balance judiciously. Specifically, the client credits are assigned by the firing intensity state before and after local model training, which reflects the difference in local data distribution from the global model. The 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 FL methods and requires fewer communication rounds.