# Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices

arXiv: https://arxiv.org/abs/2403.18607 · HTML: https://mlsanity.com/paper/2403.18607/

## Key facts

- Cited by 2 papers in the map (data through 7 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratch](https://mlsanity.com/paper/2010.01729/) (2020)
- [Federated Learning with Spiking Neural Networks](https://mlsanity.com/paper/2106.06579/) (2021)
- [Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data](https://mlsanity.com/paper/2302.06279/) (2023)
- [Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence](https://mlsanity.com/paper/1910.09594/) (2019)
- [Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition](https://mlsanity.com/paper/2205.14315/) (2022)
- [Time-Distributed Backdoor Attacks on Federated Spiking Learning](https://mlsanity.com/paper/2402.02886/) (2024)

## Built on by (most cited first, 2 of 2)

- [Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras](https://mlsanity.com/paper/2411.03022/) (2024)
- [Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attacks](https://mlsanity.com/paper/2607.27990/) (2026)

## Abstract (arXiv)

Federated neuromorphic learning (FedNL) leverages event-driven spiking neural networks and federated learning frameworks to effectively execute intelligent analysis tasks over amounts of distributed low-power devices but also perform vulnerability to poisoning attacks. The threat of backdoor attacks on traditional deep neural networks typically comes from time-invariant data. However, in FedNL, unknown threats may be hidden in time-varying spike signals. In this paper, we start to explore a novel vulnerability of FedNL-based systems with the concept of time division multiplexing, termed Spikewhisper, which allows attackers to evade detection as much as possible, as multiple malicious clients can imperceptibly poison with different triggers at different timeslices. In particular, the stealthiness of Spikewhisper is derived from the time-domain divisibility of global triggers, in which each malicious client pastes only one local trigger to a certain timeslice in the neuromorphic sample, and also the polarity and motion of each local trigger can be configured by attackers. Extensive experiments based on two different neuromorphic datasets demonstrate that the attack success rate of Spikewispher is higher than the temporally centralized attacks. Besides, it is validated that the effect of Spikewispher is sensitive to the trigger duration.