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

Spiking federated learning (FL, a distributed training method where multiple devices collaborate without sharing their private data) typically uses random selection to choose which devices contribute to the final model, but this ignores statistical heterogeneity (differences in data distribution across devices). This paper proposes SFedCA, a new strategy that assigns credits to devices based on their firing intensity (activity level in spiking neural networks, which use brain-inspired neurons that only activate when needed) before and after training, allowing better selection of devices whose data distributions match the overall model needs.