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.

翻译：脉冲联邦学习是一种新兴的分布式学习范式，它允许资源受限的设备在低功耗下协同训练而无需交换本地数据。它同时利用了联邦学习的隐私计算特性和脉冲神经网络的高能效优势。因此，它在革新多媒体数据高效处理方面极具前景。然而，现有的脉冲联邦学习方法采用随机选择的方式进行客户端聚合，假设客户端参与是无偏的。这种对统计异质性的忽视会显著影响全局模型的收敛性和准确性。在我们的工作中，我们提出了一种基于信用分配的主动客户端选择策略，即SFedCA，以明智地聚合那些有助于全局样本分布平衡的客户端。具体而言，客户端信用是通过本地模型训练前后的脉冲发放强度状态来分配的，这反映了本地数据分布与全局模型之间的差异。我们在各种非同分布且独立的数据场景下进行了全面的实验。实验结果表明，SFedCA优于现有最先进的脉冲联邦学习方法，并且需要更少的通信轮次。