Summary of Why Go Full? Elevating Federated Learning Through Partial Network Updates, by Haolin Wang et al.
Why Go Full? Elevating Federated Learning Through Partial Network Updates
by Haolin Wang, Xuefeng Liu, Jianwei Niu, Wenkai Guo, Shaojie Tang
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed Federated learning paradigm addresses the layer mismatch issue by restricting model updates to a single layer or few layers during each training round. The authors introduce the FedPart method, which outperforms traditional full network update strategies in terms of convergence speed and accuracy while reducing communication and computational overheads. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for different devices to learn together without sharing their personal data. It’s like a team effort where everyone contributes their knowledge to improve the overall performance. However, there was a problem with this method – it didn’t allow the different layers of the model to work well together. This made it slower and less accurate. To fix this issue, researchers developed a new method called FedPart. It updates only specific layers during each training round, which helps the model learn faster and more accurately. |
Keywords
» Artificial intelligence » Federated learning
