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InfoResearchPeer-reviewed
SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning
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Summary
SFedCA is a credit assignment-based active client selection strategy for spiking federated learning. It replaces random client selection with credits derived from each client's firing intensity before and after local training, which reflects how far local data departs from the global distribution. Experiments on various non-IID scenarios show it outperforms existing state-of-the-art spiking FL methods and needs fewer communication rounds.