SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning

TL;DR

SFedHIFI introduces a fire rate-based heterogeneous information fusion framework for spiking federated learning, enabling resource-adaptive models and cross-scale knowledge aggregation, resulting in improved performance and energy efficiency.

Contribution

It proposes a novel framework that allows heterogeneous SNN models in federated learning, addressing resource constraints and enhancing knowledge sharing among clients.

Findings

Outperforms baseline methods on three benchmarks.

Achieves significant energy savings over ANN-based FL.

Maintains competitive accuracy with resource-adaptive models.

Abstract

Spiking Federated Learning (SFL) has been widely studied with the energy efficiency of Spiking Neural Networks (SNNs). However, existing SFL methods require model homogeneity and assume all clients have sufficient computational resources, resulting in the exclusion of some resource-constrained clients. To address the prevalent system heterogeneity in real-world scenarios, enabling heterogeneous SFL systems that allow clients to adaptively deploy models of different scales based on their local resources is crucial. To this end, we introduce SFedHIFI, a novel Spiking Federated Learning framework with Fire Rate-Based Heterogeneous Information Fusion. Specifically, SFedHIFI employs channel-wise matrix decomposition to deploy SNN models of adaptive complexity on clients with heterogeneous resources. Building on this, the proposed heterogeneous information fusion module enables cross-scale aggregation among models of different widths, thereby enhancing the utilization of diverse local knowledge. Extensive experiments on three public benchmarks demonstrate that SFedHIFI can effectively enable heterogeneous SFL, consistently outperforming all three baseline methods. Compared with ANN-based FL, it achieves significant energy savings with only a marginal trade-off in accuracy.