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

arXiv: https://arxiv.org/abs/2602.12009 · HTML: https://mlsanity.com/paper/2602.12009/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Numerical Composition of Differential Privacy](https://mlsanity.com/paper/2106.02848/) (2021)
- [Federated Learning with Spiking Neural Networks](https://mlsanity.com/paper/2106.06579/) (2021)
- [Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence](https://mlsanity.com/paper/1910.09594/) (2019)
- [DA-LIF: Dual Adaptive Leaky Integrate-and-Fire Model for Deep Spiking Neural Networks](https://mlsanity.com/paper/2502.10422/) (2025)
- [Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence](https://mlsanity.com/paper/2504.17703/) (2025)
- [SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning](https://mlsanity.com/paper/2406.12200/) (2024)
- [The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning](https://mlsanity.com/paper/2409.12769/) (2024)

## Abstract (arXiv)

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.