# Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence

arXiv: https://arxiv.org/abs/1910.09594 · HTML: https://mlsanity.com/paper/1910.09594/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 16 of 16)

- [Federated Learning with Spiking Neural Networks](https://mlsanity.com/paper/2106.06579/) (2021)
- [Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck](https://mlsanity.com/paper/2106.01177/) (2021)
- [Bayesian Continual Learning via Spiking Neural Networks](https://mlsanity.com/paper/2208.13723/) (2022)
- [Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition](https://mlsanity.com/paper/2205.14315/) (2022)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [Fast On-Device Adaptation for Spiking Neural Networks via Online-Within-Online Meta-Learning](https://mlsanity.com/paper/2103.03901/) (2021)
- [Neuromorphic Wireless Split Computing with Multi-Level Spikes](https://mlsanity.com/paper/2411.04728/) (2024)
- [Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing](https://mlsanity.com/paper/2308.11152/) (2023)
- [Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices](https://mlsanity.com/paper/2403.18607/) (2024)
- [SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning](https://mlsanity.com/paper/2406.12200/) (2024)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [Communication Trade-offs in Federated Learning of Spiking Neural Networks](https://mlsanity.com/paper/2303.00928/) (2023)
- [The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning](https://mlsanity.com/paper/2409.12769/) (2024)
- [Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks](https://mlsanity.com/paper/2406.09680/) (2024)
- [On the Sensitivity of Firing Rate-Based Federated Spiking Neural Networks to Differential Privacy](https://mlsanity.com/paper/2602.12009/) (2026)
- [Federated Few-Shot Learning on Neuromorphic Hardware: An Empirical Study Across Physical Edge Nodes](https://mlsanity.com/paper/2603.13037/) (2026)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) offer a promising alternative to conventional Artificial Neural Networks (ANNs) for the implementation of on-device low-power online learning and inference. On-device training is, however, constrained by the limited amount of data available at each device. In this paper, we propose to mitigate this problem via cooperative training through Federated Learning (FL). To this end, we introduce an online FL-based learning rule for networked on-device SNNs, which we refer to as FL-SNN. FL-SNN leverages local feedback signals within each SNN, in lieu of backpropagation, and global feedback through communication via a base station. The scheme demonstrates significant advantages over separate training and features a flexible trade-off between communication load and accuracy via the selective exchange of synaptic weights.