# Communication Trade-offs in Federated Learning of Spiking Neural Networks

arXiv: https://arxiv.org/abs/2303.00928 · HTML: https://mlsanity.com/paper/2303.00928/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 27 Feb 2023.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Training Deep Spiking Neural Networks using Backpropagation](https://mlsanity.com/paper/1608.08782/) (2016)
- [The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks](https://mlsanity.com/paper/1910.07407/) (2019)
- [Federated learning with class imbalance reduction](https://mlsanity.com/paper/2011.11266/) (2020)
- [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)
- [Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition](https://mlsanity.com/paper/2205.14315/) (2022)
- [Adaptive Sparse Structure Development with Pruning and Regeneration for Spiking Neural Networks](https://mlsanity.com/paper/2211.12219/) (2022)
- [Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning](https://mlsanity.com/paper/2107.11415/) (2021)
- [Optimal MIMO Combining for Blind Federated Edge Learning with Gradient Sparsification](https://mlsanity.com/paper/2203.12957/) (2022)

## Built on by (most cited first, 1 of 1)

- [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)

Spiking Neural Networks (SNNs) are biologically inspired alternatives to conventional Artificial Neural Networks (ANNs). Despite promising preliminary results, the trade-offs in the training of SNNs in a distributed scheme are not well understood. Here, we consider SNNs in a federated learning setting where a high-quality global model is created by aggregating multiple local models from the clients without sharing any data. We investigate federated learning for training multiple SNNs at clients when two mechanisms reduce the uplink communication cost: i) random masking of the model updates sent from the clients to the server; and ii) client dropouts where some clients do not send their updates to the server. We evaluated the performance of the SNNs using a subset of the Spiking Heidelberg digits (SHD) dataset. The results show that a trade-off between the random masking and the client drop probabilities is crucial to obtain a satisfactory performance for a fixed number of clients.