Summary of Msfusion: a Dynamic Model Splitting Approach For Resource-constrained Machines to Collaboratively Train Larger Models, by Jin Xie et al.
MSfusion: A Dynamic Model Splitting Approach for Resource-Constrained Machines to Collaboratively Train Larger Models
by Jin Xie, Songze Li
First submitted to arxiv on: 4 Jul 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 MSfusion framework enables effective and efficient collaborative learning for training larger models on resource-constrained machines through model splitting. By assigning a subset of model parameters to each participant for local training, and aggregating with sub-models from other peers on common parameters, the framework reduces computation and communication costs. Additionally, adaptive model overlapping and contrastive loss functions are designed to maintain training effectiveness against model shift across participants. Experimental results demonstrate significant advantages in performance and efficiency for training large models, as well as strong scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MSfusion is a new way for many devices with limited resources to work together to train big artificial intelligence models. Currently, it’s hard for these devices to keep up because they don’t have enough data or computing power. The MSfusion framework helps by splitting the model into smaller parts and letting each device work on one part at a time. This makes it faster and cheaper for each device to contribute. Some extra techniques make sure that all the devices stay in sync, even when their models are slightly different. This is useful because it means many devices can work together to train big models quickly and efficiently. |
Keywords
* Artificial intelligence * Contrastive loss
