Summary of The Robustness Of Spiking Neural Networks in Communication and Its Application Towards Network Efficiency in Federated Learning, by Manh V. Nguyen et al.
The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning
by Manh V. Nguyen, Liang Zhao, Bobin Deng, William Severa, Honghui Xu, Shaoen Wu
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of Spiking Neural Networks (SNNs) to Federated Learning (FL), a collaborative model training approach. To address the communication bottleneck between local devices and remote servers, the authors propose a novel algorithm called Federated Learning with Top-K Sparsification (FLTS). This method reduces bandwidth usage by compressing model parameters while maintaining accuracy. The results demonstrate significant improvements in both communication cost and model accuracy compared to traditional Artificial Neural Networks (ANNs). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SNNs are a type of neural network that’s efficient for on-chip learning, but they’re not great at communicating with each other. In this paper, the researchers make SNNs work better for something called Federated Learning, which is like a team effort to train models. They came up with an idea called FLTS (Federated Learning with Top-K Sparsification) that makes it cheaper and faster to share information between devices while keeping the model accurate. This could be really useful in situations where lots of devices need to work together. |
Keywords
» Artificial intelligence » Federated learning » Neural network
