AI 中文总结
针对混合ANN-SNN联邦学习的表征失配问题，提出AS-FedBridge框架，通过伪脉冲接口实现信号对齐，在四个数据集上缓解异构挑战并实现性能与效率的可控权衡。
AI 中文摘要
联邦学习可在分布式边缘设备间开展协作式模型训练，同时严格保障数据隐私。为便于在资源受限的边缘设备上实际部署，脉冲神经网络（SNN）凭借其稀疏计算机制与高能效特性，成为传统人工神经网络（ANN）的极具前景的替代方案。然而，联合训练ANN与SNN会面临表征失配的挑战，该挑战本质上由信息表征的差异引发，具体体现为ANN中连续实值激活与SNN中离散时空脉冲之间的语义鸿沟。为克服这一障碍，我们提出AS-FedBridge，这是一种专为混合ANN-SNN客户端定制的新型联邦学习框架。AS-FedBridge配备了轻量级桥接模块，该模块带有伪脉冲接口，可有效将连续信号投影至脉冲兼容空间，以促进ANN-SNN对齐。由于现有混合ANN-SNN联邦框架的缺失，我们构建了一套全面基准，用于与多种先进异构联邦学习（FL）方法进行对比评估。我们的实证分析表明，ANN-SNN的对齐程度与协作FL性能呈正相关。在四个数据集上，AS-FedBridge始终展现出先进的准确率，同时缓解了极端规模、架构及客户端异构性挑战。此外，我们的框架可在模型性能与资源效率之间实现高度可控的权衡。AS-FedBridge在实现这些稳健性能提升的同时，仅引入了极少的计算开销，为混合ANN-SNN联邦学习系统奠定了稳健且实用的基础。
英文摘要
Federated learning enables collaborative model training across distributed edge devices while strictly preserving data privacy. To facilitate practical deployment on resource-constrained edge devices, Spiking Neural Networks (SNNs) have emerged as a promising alternative to traditional Artificial Neural Networks (ANNs) due to their sparse computing mechanisms and high energy efficiency. However, jointly training ANNs and SNNs exposes a challenge of representational misalignment, which is intrinsically caused by differences in information representation, specifically the semantic gap between continuous real-valued activations in ANNs and discrete spatio-temporal spikes in SNNs. To overcome this barrier, we propose AS-FedBridge, a novel federated learning framework tailored for mixed ANN-SNN clients. AS-FedBridge features a lightweight Bridge equipped with a Pseudo-Spike Interface, which effectively projects continuous signals into a spike-compatible space to facilitate ANN-SNN alignment. Given the absence of existing mixed ANN-SNN federated frameworks, we establish a comprehensive benchmark to evaluate against multiple advanced heterogeneous FL methods. Our empirical analysis demonstrates a positive correlation between the degree of ANN-SNN alignment and the collaborative FL performance. Across four datasets, AS-FedBridge consistently demonstrates advanced accuracy while mitigating extreme scale, architecture, and client heterogeneity challenge. Furthermore, our framework enables a highly controllable trade-off between model performance and resource efficiency. AS-FedBridge accomplishes these robust performance gains while introducing only marginal computational overhead, establishing a robust and practical foundation for mixed ANN-SNN federated learning systems.
