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Related Experiment Video

Updated: Jan 9, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning.

Qiugang Zhan, Jinbo Cao, Xiurui Xie

    IEEE Transactions on Neural Networks and Learning Systems
    |December 9, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces SFedCA, a novel credit assignment strategy for spiking federated learning (FL). SFedCA improves global model accuracy and convergence by selecting clients based on their data distribution, outperforming random selection methods.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Distributed Systems

    Background:

    • Spiking federated learning (FL) combines privacy-preserving FL with energy-efficient spiking neural networks (SNNs).
    • Current FL methods often use random client selection, ignoring data heterogeneity, which hinders model performance.
    • Statistical heterogeneity in client data distribution is a key challenge in federated learning.

    Purpose of the Study:

    • To propose a credit assignment-based active client selection strategy for spiking federated learning (SFedCA).
    • To address the limitations of random client selection in spiking FL by accounting for statistical heterogeneity.
    • To enhance the convergence and precision of the global model in energy-constrained distributed learning environments.

    Main Methods:

    • Developed SFedCA, a client selection strategy based on credit assignment.
    • Assigned client credits by analyzing the firing intensity state before and after local model training.
    • Evaluated SFedCA on various non-identical and independent distribution (non-IID) scenarios.

    Main Results:

    • SFedCA demonstrated superior performance compared to existing state-of-the-art spiking FL methods.
    • The proposed strategy requires fewer communication rounds for effective model training.
    • SFedCA effectively balances the global sample distribution by judiciously selecting clients.

    Conclusions:

    • Credit assignment-based client selection is effective for improving spiking federated learning.
    • SFedCA offers a more efficient and accurate approach to distributed learning with heterogeneous data.
    • This method enhances the practical applicability of spiking FL in resource-constrained settings.