Summary of Fedtlu: Federated Learning with Targeted Layer Updates, by Jong-ik Park and Carlee Joe-wong
FedTLU: Federated Learning with Targeted Layer Updates
by Jong-Ik Park, Carlee Joe-Wong
First submitted to arxiv on: 23 Dec 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 targeted layer update strategy is proposed for fine-tuning federated language models, addressing the issue of non-IID data across clients. The strategy uses a scoring mechanism to identify and update the most critical layers, avoiding noisy or poisoned updates by freezing other layers’ parameters. This approach improves convergence and performance in non-IID settings, offering an efficient method for fine-tuning federated language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning lets many devices train a single language model without sharing their data. But when the devices have different kinds of data, it makes training harder. This paper finds a way to make the training better by only updating the most important parts of the language model. It does this by looking at how well each part of the model is working and choosing which ones to update. This helps the model learn faster and work better when the devices have different kinds of data. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Language model
