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arXiv · 2602.12009

On the Sensitivity of Firing Rate-Based Federated Spiking Neural Networks to Differential Privacy

Luiz Pereira·Mirko Perkusich·Dalton Valadares·Kyller Gorgônio
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Abstract

Federated Neuromorphic Learning (FNL) enables energy-efficient and privacy-preserving learning on devices without centralizing data. However, real-world deployments require additional privacy mechanisms that can significantly alter training signals. This paper analyzes how Differential Privacy (DP) mechanisms, specifically gradient clipping and noise injection, perturb firing-rate statistics in Spiking Neural Networks (SNNs) and how these perturbations are propagated to rate-based FNL coordination. On a speech recognition task under non-IID settings, ablations across privacy budgets and clipping bounds reveal systematic rate shifts, attenuated aggregation, and ranking instability during client selection. Moreover, we relate these shifts to sparsity and memory indicators. Our findings provide actionable guidance for privacy-preserving FNL, specifically regarding the balance between privacy strength and rate-dependent coordination.

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Luiz Pereira, Mirko Perkusich, Dalton Valadares, Kyller Gorgônio. 2026-02-12. On the Sensitivity of Firing Rate-Based Federated Spiking Neural Networks to Differential Privacy. https://arxiv.org/abs/2602.12009

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