Summary of Rethinking the Starting Point: Collaborative Pre-training For Federated Downstream Tasks, by Yun-wei Chu et al.
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks
by Yun-Wei Chu, Dong-Jun Han, Seyyedali Hosseinalipour, Christopher G. Brinton
First submitted to arxiv on: 3 Feb 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 In this study, researchers address the limitations of current pre-training methods for federated learning (FL) by proposing a collaborative/distributed approach called CoPreFL. The key innovation is a model-agnostic meta-learning (MAML) procedure that tailors the global model to mimic heterogeneous and unseen FL scenarios. This results in a robust initialization for downstream FL tasks that adapts rapidly to arbitrary tasks. The MAML procedure incorporates performance variance into the meta-objective function, balancing performance across clients rather than solely optimizing for accuracy. Experimental results demonstrate significant improvements in both average accuracy and variance compared to various pre-training baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoPreFL is a new way to make models better at learning from lots of different places. Right now, some models are good at starting with information, but they don’t do well when there’s new data. This makes it hard for them to learn from all the different places where people might have devices. The researchers made a plan called CoPreFL that helps make these models better. It works by making a copy of the model and then changing it so it looks like what would happen in different places. This helps the model be more ready to learn when there’s new data. The results show that this way is much better than other ways we’ve tried. |
Keywords
* Artificial intelligence * Federated learning * Meta learning * Objective function
