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

Ran Tao; Qiugang Zhan; Shantian Yang; Xiurui Xie; Qi Tian; Guisong Liu

cs.LGMar 16, 2026

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

Authors: Ran Tao, Qiugang Zhan, Shantian Yang, Xiurui Xie, Qi Tian, Guisong Liu

Organizations: Complex Laboratory of New Finance and Economics, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, China · Engineering Research Center of Intelligent Finance, Ministry of Education, China · Kash Institute of Electronics and Information Industry, China · Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China, China ·

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.

Figures & tables

Figure 1: Overview of SFedHIFI. First, local clients are clustered into the model scale set of computational resources $\mathbf{p}=\{0.25,0.5,075,1.0\}$ . Then, the local $(\mathbf{p}\times)$ model gets the uniform shared basis $B$ and the model scale factor $M^{\mathbf{p}}$ through the Channel-wise Matrix Decomposition. In the central server, the uniform shared basis will be aggregated as the global $\overline{B}$ , and the model scale factors will be aggregated into global $\{\overline{M}^{\mathbf{p}}\}$ corresponding to different model scales. The list of global model scale factors $\{\overline{M}^{\mathbf{p}}\}$ is then fed to the HIFI module, where the Firing Rate-Based Heterogeneous Information Fusion is applied to integrate knowledge across scales. This process enhances the representational capacity of global SNNs at all scales and improves their overall performance.

Dataset	Scale	IID				Non-IID Dir(0.5)				Non-IID Dir(0.3)
Dataset	Scale	FedAVG	FedProx	FedNova	SFedHIFI	FedAVG	FedProx	FedNova	SFedHIFI	FedAVG	FedProx	FedNova	SFedHIFI
Fasion-MNIST	$0.25\times$	90.50	89.10	88.48	91.46	85.19	89.21	88.59	88.70	83.35	86.24	87.74	87.27
	$0.5\times$	91.12	89.56	84.85	92.38	85.36	88.56	87.63	89.85	83.86	88.70	87.97	88.31
	$0.75\times$	91.47	89.68	73.70	92.03	88.18	89.69	89.82	90.41	86.14	88.91	89.31	88.99
	$1.0\times$	91.17	90.53	90.20	92.43	89.91	90.79	90.74	90.32	87.98	88.29	88.63	89.18
	Avg.	91.06	89.97	84.31	92.08	87.16	89.56	89.20	89.82	85.33	88.04	88.41	88.43

Table 1: Experimental results on Benchmark Comparison Evaluation. We report classification performance using the standard Top-1 accuracy (%) as the evaluation metric. The column labeled ”Avg.” denotes the average accuracy across all model scales, providing a comprehensive view of overall performance. Results highlighted in ” numbers ” indicate the best performance achieved within each comparison group.

Scale	IID				Non-IID Dir(0.5)				Non-IID Dir(0.3)
Scale	$\text{HIFI}_{w/o}$	$\text{HIFI}_{\text{fixed}}$	$\text{HIFI}_{\text{rand}}$	SFedHIFI	$\text{HIFI}_{w/o}$	$\text{HIFI}_{\text{fixed}}$	$\text{HIFI}_{\text{rand}}$	SFedHIFI	$\text{HIFI}_{w/o}$	$\text{HIFI}_{\text{fixed}}$	$\text{HIFI}_{\text{rand}}$	SFedHIFI
$0.25\times$	83.82	82.11	77.72	85.64	80.65	74.25	73.60	80.83	74.52	68.94	58.57	79.35
$0.5\times$	88.83	89.07	88.36	89.63	84.87	85.90	83.56	87.12	76.16	82.20	72.70	86.63
$0.75\times$	90.55	91.06	89.96	91.14	85.55	88.94	86.61	89.33	82.68	82.26	79.19	86.03
$1.0\times$	91.62	91.81	91.15	92.06	85.42	89.08	85.98	89.26	79.29	84.39	59.46	86.90
Avg.	88.71	88.52	86.80	89.62	84.12	84.54	82.44	86.64	78.16	79.45	67.48	84.73

Table 2: Ablation experiment results of the HIFI module on CIFAR-10. We still report classification performance using the standard Top-1 accuracy (%) as the evaluation metric.

Metric	Scale	IID		Dir(0.5)		Dir(0.3)
Metric	Scale	ANNs	Ours	ANNs	Ours	ANNs	Ours
Accuracy (%) $\uparrow$	$0.25\times$	89.75	91.46	90.91	88.70	90.62	87.27
	$0.5\times$	90.14	92.38	91.74	89.85	91.00	88.31
	$0.75\times$	90.90	92.03	91.33	90.41	91.04	88.99
	$1.0\times$	91.01	92.43	91.90	90.32	91.56	89.18
	Avg.	90.45	92.08	91.47	89.82	91.06	88.43

Table 3: Comparison between SFedHIFI and ANN in Accuracy and Energy Consumption under Identical Network Configurations on Fashion-MNIST.

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Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions

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Department of Electrical Engineering, Uppsala University, Sweden

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Spiking neural networks (SNNs) are biologically inspired energy-efficient models that use sparse binary spike-based communication between neurons, making them attractive for resource-constrained edge devices. Federated learning enables such devices to train collaboratively without sharing raw data. In time-series applications, edge devices often collect data at different time resolutions due to hardware and energy constraints. This temporal heterogeneity poses a fundamental challenge for federated learning: parameters learned at one temporal resolution do not necessarily transfer directly to another, which might result in the naive federated averaging being ineffective. Targeting SNNs and, more broadly, deep networks with stateful neurons, we propose a federated learning framework that addresses this temporal resolution mismatch. We investigate how neuron parameters learned from data at different temporal resolutions and model aggregation should be integrated. We evaluate the proposed framework across two SNN-native benchmark datasets (SHD and DVS-Gesture) under a range of resolution heterogeneity scenarios. Our results show that the proposed adaptation methods can substantially recover accuracy lost due to temporal mismatch, hence enabling each client to train at their local temporal resolution while remaining compatible with the global model.

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