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This paper introduces AS-FedBridge, a federated learning framework designed to address the representational misalignment between Artificial Neural Networks (ANNs) and Spiking Neural Networks (SNNs) in heterogeneous environments. By utilizing a lightweight Bridge with a Pseudo-Spike Interface, the framework effectively translates continuous signals into a spike-compatible format, enhancing collaborative performance across mixed ANN-SNN clients. Empirical results across four datasets reveal that AS-FedBridge not only improves accuracy but also maintains efficiency, demonstrating a significant correlation between ANN-SNN alignment and federated learning success.
Bridging the gap between ANNs and SNNs could revolutionize federated learning on resource-constrained devices, achieving high accuracy without sacrificing efficiency.
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.
