Summary of Spafl: Communication-efficient Federated Learning with Sparse Models and Low Computational Overhead, by Minsu Kim et al.
SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead
by Minsu Kim, Walid Saad, Merouane Debbah, Choong Seon Hong
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 A novel federated learning (FL) framework, SpaFL, is proposed to address the communication and computation overhead challenges in FL. SpaFL optimizes sparse model structures with low computational overhead by defining a trainable threshold for each filter/neuron to prune its connected parameters. The pruning process itself is optimized by only communicating thresholds between the server and clients, learning how to prune. Additionally, global thresholds are used to update model parameters based on aggregated parameter importance. The paper derives a generalization bound for SpaFL, providing insights into the relationship between sparsity and performance. Experimental results show that SpaFL improves accuracy while requiring significantly less communication and computing resources compared to sparse baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SpaFL is a new way to make federated learning work better on devices with limited power and data. It does this by using a special technique called pruning, which removes parts of the model that aren’t needed. This makes it faster and uses less energy. The researchers also came up with a clever way to update the model parameters without sending all the data back and forth. They tested SpaFL and found that it’s more accurate than other methods while using much less power and data. |
Keywords
» Artificial intelligence » Federated learning » Generalization » Pruning
