Summary of Exploring Selective Layer Fine-tuning in Federated Learning, by Yuchang Sun and Yuexiang Xie and Bolin Ding and Yaliang Li and Jun Zhang
Exploring Selective Layer Fine-Tuning in Federated Learning
by Yuchang Sun, Yuexiang Xie, Bolin Ding, Yaliang Li, Jun Zhang
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper explores selective layer fine-tuning in federated learning (FL), a privacy-preserving approach for fine-tuning foundation models using distributed data. Clients can choose a subset of model layers to fine-tune based on their task-specific data, rather than the entire model, due to limited computational resources. The study demonstrates that layer selection has a significant impact on model convergence, influencing both the importance of selected layers and heterogeneous choices across clients. A strategic layer selection method is proposed, utilizing local gradients and regulating layer selections, which outperforms several baselines in experiments on image and text datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps computers learn together without sharing their data. Sometimes, computers can only process a little bit of information before they get tired. This paper talks about how to make the most of this limited processing power by choosing which parts of the computer’s “brain” (called layers) to update. They found that choosing the right layers makes a big difference in how well the computer learns and adapts to different data. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning
